introduction to problem solving and software development

What Is Problem Solving? How Software Engineers Approach Complex Challenges

From debugging an existing system to designing an entirely new software application, a day in the life of a software engineer is filled with various challenges and complexities. The one skill that glues these disparate tasks together and makes them manageable? Problem solving . 

Throughout this blog post, we’ll explore why problem-solving skills are so critical for software engineers, delve into the techniques they use to address complex challenges, and discuss how hiring managers can identify these skills during the hiring process. 

What Is Problem Solving?

But what exactly is problem solving in the context of software engineering? How does it work, and why is it so important?

Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow. It could be something as simple as figuring out why a piece of code isn’t working as expected, or something as complex as designing the architecture for a new software system. 

In a world where technology is evolving at a blistering pace, the complexity and volume of problems that software engineers face are also growing. As such, the ability to tackle these issues head-on and find innovative solutions is not only a handy skill — it’s a necessity. 

The Importance of Problem-Solving Skills for Software Engineers

Problem-solving isn’t just another ability that software engineers pull out of their toolkits when they encounter a bug or a system failure. It’s a constant, ongoing process that’s intrinsic to every aspect of their work. Let’s break down why this skill is so critical.

Driving Development Forward

Without problem solving, software development would hit a standstill. Every new feature, every optimization, and every bug fix is a problem that needs solving. Whether it’s a performance issue that needs diagnosing or a user interface that needs improving, the capacity to tackle and solve these problems is what keeps the wheels of development turning.

It’s estimated that 60% of software development lifecycle costs are related to maintenance tasks, including debugging and problem solving. This highlights how pivotal this skill is to the everyday functioning and advancement of software systems.

Innovation and Optimization

The importance of problem solving isn’t confined to reactive scenarios; it also plays a major role in proactive, innovative initiatives . Software engineers often need to think outside the box to come up with creative solutions, whether it’s optimizing an algorithm to run faster or designing a new feature to meet customer needs. These are all forms of problem solving.

Consider the development of the modern smartphone. It wasn’t born out of a pre-existing issue but was a solution to a problem people didn’t realize they had — a device that combined communication, entertainment, and productivity into one handheld tool.

Increasing Efficiency and Productivity

Good problem-solving skills can save a lot of time and resources. Effective problem-solvers are adept at dissecting an issue to understand its root cause, thus reducing the time spent on trial and error. This efficiency means projects move faster, releases happen sooner, and businesses stay ahead of their competition.

Improving Software Quality

Problem solving also plays a significant role in enhancing the quality of the end product. By tackling the root causes of bugs and system failures, software engineers can deliver reliable, high-performing software. This is critical because, according to the Consortium for Information and Software Quality, poor quality software in the U.S. in 2022 cost at least $2.41 trillion in operational issues, wasted developer time, and other related problems.

Problem-Solving Techniques in Software Engineering

So how do software engineers go about tackling these complex challenges? Let’s explore some of the key problem-solving techniques, theories, and processes they commonly use.

Decomposition

Breaking down a problem into smaller, manageable parts is one of the first steps in the problem-solving process. It’s like dealing with a complicated puzzle. You don’t try to solve it all at once. Instead, you separate the pieces, group them based on similarities, and then start working on the smaller sets. This method allows software engineers to handle complex issues without being overwhelmed and makes it easier to identify where things might be going wrong.

Abstraction

In the realm of software engineering, abstraction means focusing on the necessary information only and ignoring irrelevant details. It is a way of simplifying complex systems to make them easier to understand and manage. For instance, a software engineer might ignore the details of how a database works to focus on the information it holds and how to retrieve or modify that information.

Algorithmic Thinking

At its core, software engineering is about creating algorithms — step-by-step procedures to solve a problem or accomplish a goal. Algorithmic thinking involves conceiving and expressing these procedures clearly and accurately and viewing every problem through an algorithmic lens. A well-designed algorithm not only solves the problem at hand but also does so efficiently, saving computational resources.

Parallel Thinking

Parallel thinking is a structured process where team members think in the same direction at the same time, allowing for more organized discussion and collaboration. It’s an approach popularized by Edward de Bono with the “ Six Thinking Hats ” technique, where each “hat” represents a different style of thinking.

In the context of software engineering, parallel thinking can be highly effective for problem solving. For instance, when dealing with a complex issue, the team can use the “White Hat” to focus solely on the data and facts about the problem, then the “Black Hat” to consider potential problems with a proposed solution, and so on. This structured approach can lead to more comprehensive analysis and more effective solutions, and it ensures that everyone’s perspectives are considered.

This is the process of identifying and fixing errors in code . Debugging involves carefully reviewing the code, reproducing and analyzing the error, and then making necessary modifications to rectify the problem. It’s a key part of maintaining and improving software quality.

Testing and Validation

Testing is an essential part of problem solving in software engineering. Engineers use a variety of tests to verify that their code works as expected and to uncover any potential issues. These range from unit tests that check individual components of the code to integration tests that ensure the pieces work well together. Validation, on the other hand, ensures that the solution not only works but also fulfills the intended requirements and objectives.

Explore verified tech roles & skills.

The definitive directory of tech roles, backed by machine learning and skills intelligence.

Explore all roles

Evaluating Problem-Solving Skills

We’ve examined the importance of problem-solving in the work of a software engineer and explored various techniques software engineers employ to approach complex challenges. Now, let’s delve into how hiring teams can identify and evaluate problem-solving skills during the hiring process.

Recognizing Problem-Solving Skills in Candidates

How can you tell if a candidate is a good problem solver? Look for these indicators:

• Previous Experience: A history of dealing with complex, challenging projects is often a good sign. Ask the candidate to discuss a difficult problem they faced in a previous role and how they solved it.
• Problem-Solving Questions: During interviews, pose hypothetical scenarios or present real problems your company has faced. Ask candidates to explain how they would tackle these issues. You’re not just looking for a correct solution but the thought process that led them there.
• Technical Tests: Coding challenges and other technical tests can provide insight into a candidate’s problem-solving abilities. Consider leveraging a platform for assessing these skills in a realistic, job-related context.

Assessing Problem-Solving Skills

Once you’ve identified potential problem solvers, here are a few ways you can assess their skills:

• Solution Effectiveness: Did the candidate solve the problem? How efficient and effective is their solution?
• Approach and Process: Go beyond whether or not they solved the problem and examine how they arrived at their solution. Did they break the problem down into manageable parts? Did they consider different perspectives and possibilities?
• Communication: A good problem solver can explain their thought process clearly. Can the candidate effectively communicate how they arrived at their solution and why they chose it?
• Adaptability: Problem-solving often involves a degree of trial and error. How does the candidate handle roadblocks? Do they adapt their approach based on new information or feedback?

Hiring managers play a crucial role in identifying and fostering problem-solving skills within their teams. By focusing on these abilities during the hiring process, companies can build teams that are more capable, innovative, and resilient.

Key Takeaways

As you can see, problem solving plays a pivotal role in software engineering. Far from being an occasional requirement, it is the lifeblood that drives development forward, catalyzes innovation, and delivers of quality software. 

By leveraging problem-solving techniques, software engineers employ a powerful suite of strategies to overcome complex challenges. But mastering these techniques isn’t simple feat. It requires a learning mindset, regular practice, collaboration, reflective thinking, resilience, and a commitment to staying updated with industry trends. 

For hiring managers and team leads, recognizing these skills and fostering a culture that values and nurtures problem solving is key. It’s this emphasis on problem solving that can differentiate an average team from a high-performing one and an ordinary product from an industry-leading one.

At the end of the day, software engineering is fundamentally about solving problems — problems that matter to businesses, to users, and to the wider society. And it’s the proficient problem solvers who stand at the forefront of this dynamic field, turning challenges into opportunities, and ideas into reality.

This article was written with the help of AI. Can you tell which parts?

Get started with HackerRank

Over 2,500 companies and 40% of developers worldwide use HackerRank to hire tech talent and sharpen their skills.

Recommended topics

• Hire Developers
• Problem Solving

Does a College Degree Still Matter for Developers in 2024?

• All Articles
• Let's Connect
• Fundamentals
• Soft Skills
• Side Projects

A Guide to Problem-Solving for Software Developers with Examples

If I ask you, out of the blue, what’s the role of a developer, what would you answer? Coding all day? Drinking coffee? Complaining about the management?

To me, a developer is first and foremost a problem solver, simply because solving problem is the most important (and the most difficult) part of our job. After all, even if our code is perfect, clear, performing great, a masterpiece of form and meaning, it’s useless if it doesn’t solve the problem it was meant to solve.

So, let’s dive into problem-solving today. More specifically, we’ll see in this article:

• How to define a problem, and the difference sometimes made between problem-solving and decision-making.
• Why some problems should not be solved.
• The two wide categories of problems you can encounter.
• Why it’s important to correctly define the problem, and how to do so.
• How to explore the solution space.
• Why deferring a problem might be the best decision to make in specific situations.
• Why reflecting on the whole process afterward can help you in the future.

This article is mostly based on my own experience, even if I apply here some ideas I found in books and papers.

We have our plan. Now, it’s time to dive deep into the difficult, but rewarding, process of problem-solving.

Problem-Solving and Decision-Making

“When I use a word,” Humpty Dumpty said in rather a scornful tone, “it means just what I choose it to mean — neither more nor less.” “The question is,” said Alice, “whether you can make words mean so many different things.” “The question is,” said Humpty Dumpty, “which is to be master — that’s all.” Lewis Caroll Source

Words are ambiguous; they can mean different things for each of us. So let’s first begin to agree on the definition of “problem-solving” here, to be sure we’re on the same page.

Let’s first look at the definition of the word “problem” in a dictionary:

• According to the American Heritage Dictionary , a problem is “a question to be considered, solved, or answered”.
• According to the Oxford Learner’s dictionary , a problem is “a thing that is difficult to deal with or to understand”.

In short, in any problem, there is some degree of uncertainty. If you’re certain of the solution, the problem is already solved. Nothing would need to be “considered, solved, or answered”.

Information is useful to reduce this uncertainty. The quantity is often not the most important, but the quality will be decisive. If I tell you that 90% of my readers are extremely intelligent, would it help you to solve a problem in your daily job? I bet it wouldn’t. It’s information nonetheless, but its usefulness for you is close to zero.

This is an extreme example, but it highlights an important point: before collecting any data, define your problem clearly; then, according to the problem, decide what data you need. Yet, many companies out there begin to collect the data and then decide what problem to solve. We’ll come back to that soon in this article.

So, to summarize, a problem is a situation with some degree of uncertainty. Sometimes, this uncertainty needs to be reduced to come up with an appropriate solution, or, at least, a decision to move forward to your specific goal.

Is there a Problem to Solve?

Whenever you (or somebody else) see a problem, you should always ask yourself this simple question first: is it really a problem, and should we solve it now ?

In other words, ask yourself the following questions:

• Why is this problem important to solve?
• Would be solving the problem creates some value? What value?
• What would happen if the problem was not solved?
• What desired outcome do we expect by solving the problem?

If the problem doesn’t bother anybody and solving it doesn’t create any value, why allocating effort and time to solve it?

It sounds obvious, but it’s an important point nonetheless. More often than not, I see developers heading first in solving problems without asking themselves if they should solve them at the first place.

The most common examples I can think of are useless refactoring. I saw developers refactoring parts of codebases which never change, or is rarely executed at runtime. In the mind of the developer, the code itself is the problem: refactoring is the solution.

I remember a similar case: a developer refactored part of the codebase which was basically never used. We discovered, months later, when we had more and more users using this specific part of the codebase, that the refactoring didn’t really simplify anything. To the contrary; we had to refactor the code again. The first refactoring tried to solve a problem which didn’t exists.

Of course, the developer could argue that the value created is a “cleaner” codebase, but it’s arguable, especially when the code is neither often modified nor used. The value created here is not clear, and it would have been easier if the first refactoring never happened. In this specific situation, I recommend refactoring when you actively change part of the codebase for another reason (implementing a new feature for example).

Whether a problem is worthy to be solved is subjective. It also depends on the problem: if the solution is clear and straightforward, it might be useful to solve it, if the consequences of the solution are also clearly known and the risks are low. Unfortunately, these kinds of problems, in practice, are quite rare.

Types of Problems

I would define here two wide categories of problems: the problems with a (or multiple) clear solution (what the literature call “problem-solving”), and the problems without clear solution (it’s sometimes called “decision-making” instead of “problem-solving”).

In fact, if the problem you’re trying to solve has a clear, accepted answer, it’s very likely it has been solved already. It’s often the case for mechanical, technical problems. For example, let’s say that you need to order a list; you just have to search on the wild Internet how to do so in your programming language of choice, and you’re done! You can ask an “AI” too, or stack overflow, or whatever.

In my experience, most technical problems have one (or multiple) accepted solution. I won’t speak about these kinds of problems at length in this article, since they’re the easiest to solve.

When you’re in front of a problem which has no clear solution (even after doing some research), it’s where things get more complicated. I’d argue that most problems you’ll face, as a software developer, are of this category. Problems which are directly linked to the domain of the company you work with are often specific (because they depend on the domain), and complex.

For example, I’m working for a company providing a learning platform for medical students who want to become doctors, among other services. This context is changing because the real world is changing; medicine is no exception.

Recently, we had to create new data structures for the knowledge we provide; these data structures are directly linked to the domain (medicine) here. But what data structures to create? How can they adapt to the ever-changing environment? How to capture the data in the most meaningful way, with understandable naming for other developers?

Decisions had to be made, and when there are no clear solutions, you need to come up with a couple of hypothesizes. They won’t feel necessary like solutions , but rather decisions to take to move forward toward the desired outcome. It often ends up in compromises, especially if you’re working in a team where the members have different opinions .

Also, architectural decisions have often no clear solutions because they depend, again, on the changing context. How to be sure that an architectural decision is good today and in three months? How can we make the architecture flexible enough to adapt to the blurry future?

As developers, we deal with complex codebases, which are somewhat linked to the even more complex real world. It’s difficult to know beforehand the consequences of our decisions, as well as the benefits, the drawback, and the potential bugs we introduce.

Before jumping into the solution space however, we first need a good detour in the problem space.

Defining the Problem

Correctly stating the problem.

After determining that we indeed have some kind of problem, it’s tempting to try to find a solution directly. Be patient: it’s better to look at the problem more closely first.

If you don’t specify well the problem, you might not solve it entirely. It’s also possible that you end up solving the wrong problem, or the symptoms of a problem, that is, other minor problems created by a root problem. Often, the ideal scenario is to find the root problem, even if you don’t want to tackle it first. In any case, it’s always useful information.

For example, not long ago, our users didn’t find the content they were searching for, using our search functionality on our learning platform.

We could have directly solved the problem by asking the search team to adjust that for us, but this problem was only a symptom. It wasn’t the first time that we had to spend time and energy trying to communicate to the search team what we wanted to fix; the real root problem here was that we didn’t have any ownership of our search results.

The solution: we created a better API communicating with the search team, to be able to adjust ourselves the search results in a more flexible manner.

When looking at a problem, a good first step is to write it down. Don’t do it once; try to find different formulations for the same problem.

Writing is nice (I love it!), but other ways to represent ideas can be really useful too. You can try to draw what you understand from the problem: a drawing, a diagram, or even a picture can help you understand the problem.

From there, you can ask yourself: do you have enough information to take a decision? The answer will be mostly based on the experience of the problem solver, there is no magical formula to be sure that you can and will solve the problem.

You should also try to look at the problem from different angles, to really frame it correctly. The best way to do so is to solve problems as a team.

Solving Problems in a Team

Trying to describe and think about a problem is a great beginning, but it’s even better if you do it as a team. You can exchange experience, opinions, and it’s easier to look at a problem from multiple angles when multiple developers are involved.

First, make sure that everybody in the team is aware of the problem. Defining it altogether is the best. If you have a doubt that somebody is not on the same page, you can re-explain it using different words. It might bring more insights and ideas to the discussion.

Don’t assume that everybody understands the problem equally. Words are powerful, but they are also ambiguous; never hesitate to ask questions (even if they seem stupid at first), and encourage the team to do the same. If your colleagues see that you’re not afraid to ask, it will give them confidence to do the same.

The ambiguity can also build overtime, after the problem was discussed. That’s why it’s really important to document the whole process, for anybody to be able to look at it again and fix the possible creeping misconceptions. Don’t try to describe everything, but try to be specific enough. It’s a delicate balance, and you’ll get better at it with experience.

If you don’t like writing, I’d recommend you to try anyway: this is a powerful skill which will be useful in many areas of your life.

Regarding the team of problem solvers, diversity is important. Diversity of opinion, experience, background, you name it. The more diverse the opinions and ideas are, the more chances you’ll have to solve the problem satisfyingly (more on that later). If the members of the team have enough respect, humility, and know how to listen to their colleagues , you’re in the perfect environment to solve problems.

As developers, we’re dealing with moving systems, because they need to reflect the ever-changing business domain of the company you’re working with. These problems are unique, and even if similar problems might have been solved in the past, they’re never the exactly same. The differences can have an impact on the solution, sometimes insignificant (allowing you to re-apply the solution found previously), sometimes important enough to change the solution entirely.

Exploring the Solution Space

Now that we’ve defined the problem, thought about it with our team, tried to look at it from different angles, it’s time to try to find solutions, or at least to make a decision.

What is a good decision? The one which will bring you closer to your desired outcome. It sounds obvious, but there can be some ego involved in discussions, which will push us to try to be right even if it’s not the best solution in the current context. Our personal incentives can conflict with the company’s best interest; it’s always good to try to stay aware of that.

The solution should also be the simplest possible, while still moving forward to the desired outcome. It should also have an acceptable level of risk when we decide to apply the solution. In my experience, complicated solutions are the ones which come up first: don’t stop there. Take some time trying to find the best solution with your team.

For example, here’s what we do with my actual team:

• We define the problem altogether.
• We try to think about different hypothesizes. Not only one, but a couple of them.
• We write the benefits and drawbacks of each hypothesis (which can lead to more ideas, and possibly more hypothesizes).
• We commit to a hypothesis, which then needs to be implemented.

What I meant by “hypothesis” here is a solution which might work; but only the implementation of the hypothesis can be considered as a solution. Before the implementation, it’s just an informed guess. Many things can go wrong during an implementation.

This process looks simple, but when you have multiple developers involved, it’s not. Again, if each member of the team have good soft skills and some experience, it can be an enjoyable and rewarding process. But you need a good team for it to work efficiently (that’s why it’s so important to ask the good questions when joining a company). It’s even better if the members of the team are used to swim in uncertainty, and take it as a challenge more than a chore.

The process described above is just an example; in practice it’s often more chaotic. For example, even when a decision is made, your brain might still continue to process the problem passively. If you find some flaws in the hypothesis you’ve committed to, congratulations! You have now a brand-new problem.

I can’t emphasize it enough: try to be as detached as possible from your ideas, opinions, and preferred hypothesizes. The goal is not for you to be right and feel good, but for your company to move in the good direction. It’s hard, but with practice it gets easier.

I also want to underline the importance of finding both benefits and drawbacks for the different hypothesizes you (and your team) came up with.

To find good solutions, we might also need to reduce the uncertainty around their possible consequences. Doing some external research can help, like gathering data around the problem and the possible hypothesizes. In the best case scenario, if you can find enough data, and if you feel confident that you can move forward with a hypothesis, that’s already a great victory.

If you don’t have enough external information to reduce the uncertainty to a level you feel comfortable with, look at your past experience. Try to find problems similar to the one your deal with in the present, and try to think about the solutions applied at the time, to see if they could also be applied in your current case. But be careful with this approach: complex problems are context-sensitive, and the context you were in the past will never be exactly the same as the present and future contexts.

For example, I recently changed the way we display search results in our system, because we had some data indicating that some users had difficulties to find what they really wanted to find. The problem: users have difficulties to find the good information; it’s a recurrent problem which might never be 100% solved. That said, thanks to the data gathered, we found an easy way to improve the situation.

The data was very clear and specific, but it’s not always the case. More often than not, your data won’t really prove anything. It might only show correlations without clear causality. It will be even more true if you begin by gathering data without defining first the problem you try to solve. You can find problems looking at some data, that’s true, but it needs care and deep understanding of what you’re doing; looking at data when you know exactly what you want to solve works better.

Using this kind of process, the hypothesis is often some sort of compromise. That’s fine; committing to a hypothesis is not the end of the process, and there will be other occasions to revisit and refine the solution.

If you don’t feel comfortable with the level of uncertainty of the problem (or the risk involved by applying your hypothesis), you need to dig more. Writing a prototype can be useful for example, if you hesitate between two or more approaches. If your prototype is convincing enough, it can also be useful to gather feedback from your users, even if the ones testing your hypothesis will always be more invested if they test a real-life functionality, instead of a prototype which might use dummy data, or be in a context which is too remote from the “real” context.

In my opinion, prototypes are not always useful for complex problems, because a prototype only test a new feature at time T, but doesn’t allow you to see if the solution stay flexible enough overtime. That’s often a big concern: how will the solution evolve?

But prototyping can still help gather information and reduce the uncertainty of the problem, even if the prototype doesn’t really give you the solution on a silver platter. It’s also great for A/B testing, when you’re in the (likely) case when you have not much information about the real needs of your users. You could ask them of course, but nothing guarantee that they know themselves what these needs are.

If you don’t find any satisfying hypothesis to your problem, you might also challenge the desired outcome. Maybe a similar, simplest hypothesis, with slightly different outcomes, could work better? If it makes things easier, faster, and less complex, it could be the best solution. Don’t hesitate to challenge your stakeholders directly on the desired outcomes.

Deferring the Problem

In some cases, you might be hesitant to try to solve a problem if there is still too much uncertainty around it. In that case, it might be best to defer solving the problem altogether.

Deferring the problem means that you don’t solve it now ; you keep things as they are, until you get more information to reduce the uncertainty enough.

We had a problem in the company I worked with some time ago: we have dosages which can be discovered in articles, but users didn’t really find them, and nobody really knew why. Because of this lack of information, the problem was not tackled right away, but differed. From there, data have been collected overtime, allowing us to understand the scope of the problem better.

Don’t forget that deferring a problem is already taking a decision. It might be the less disruptive decision for the application and its codebase, but it’s s decision nonetheless, and it can have consequences. Seeing a differed problem as a decision will push you to think about the possible consequences of your inaction, and you’ll look at it as a partial “solution”, with some uncertainty and risk associated to it.

In my experience, deferring the problem works well only when you try to actively seek more data to solve it later. It can be some monitoring to see how the problem evolves, or some data taken from users’ actions. Sometimes, simply waiting can also give you important information about the nature of the problem.

What you shouldn’t do is try to forget the problem. It might come back in force to haunt your sleepless nightmares later. Avoiding a problem is not deferring it.

Here’s another example: we began recently to build some CMS tooling for medical editors, for them to write and edit content on our learning platform. We had one GraphQL API endpoint at the beginning, providing data to two different part of the application:

• Our CMS for medical editors.
• Our learning platform for medical students.

We knew that using one single GraphQL endpoint for these two types of users could cause some problems.

But we didn’t do anything about it, mostly because we didn’t see any real, concrete problem, at least at first. When a minor symptom, related to this unique endpoint, popped up, we spoke about it, and we still chose not to do anything. We preferred deferring the problem once more, to try to solve the real problem (one API for two different kinds of applications) later.

Finally, when we had enough symptoms and some frustration, we decided to split our graphQL API in two different endpoints. It was the best moment to do so: we had enough information to come up with a good decision, we applied it, and we stayed vigilant, to see how our applied hypothesis would evolve.

Moving fast and breaking things is not always the best solution. In some situations, waiting a bit and see how things evolve can allow you to solve your problems in a more effective way. But, as always, it depends on the problem, its context, and so on.

Reading this article, you might have wondered: how much information is enough to be comfortable enough to apply a solution? Well, again, your experience will be the best judge here. You’ll also need to consider carefully risks, benefits, and drawbacks. It doesn’t mean that you need to chicken out if you don’t have 100% certainty about a problem and some hypothesizes; being a software developer implies to have some courage and accept that mistakes will be made. It’s not an easy task, and there is no general process to follow in any possible case.

In short: use your brain. Even if you’re totally wrong, you’ll have the opportunity to fix the bad decisions you’ve made before the implementation, during the implementation, and even after it. We don’t code in stone.

The Implementation: The Value of Iteration

You’ve gathered with your team, tried to define the problem, found multiple hypothesizes, and agreed to try one of them. Great! Problem solved.

Not so fast! We still need to apply the hypothesis, and hope that it will become a good solution to the problem. Doing so, you’ll gather more information along the way, which might change your perspective on the problem, on your hypothesizes, and can even create some baby problems on its own.

It’s where the agile methodology is useful: since we’ll never have 100% certainty regarding a problem and its possible solution, we’ll learn more about both while implementing the hypothesis. That’s why it’s so valuable to iterate on the implementation: it gives you more information to possibly adjust your code, or even the problem, or even switching hypothesizes altogether. Who knows? A solution which is not implemented is just a guess.

If the hypothesis applied is not the ones you would have personally preferred (compromising, or even giving up on your preferred solution is common in a team), only applying it will tell you if you’re right or wrong; that is, if the hypothesis can become a solution solving the problem, at least in the present context.

If you’re worried about how a specific solution will evolve overtime, it’s more complicated, because an implementation won’t give you the information you seek. Still, implementing a hypothesis can be a great source of learning (the most valuable to me is when I’m wrong, because I learn even more). If you think that your hypothesis can have better outcome at time T, you might also try to implement it and compare it. Again, it’s where prototyping is useful.

When applying the solution, you need to look at the details of the implementation, as well as the big picture, to judge if the solution you’re creating is appropriate (leading to the desired outcome). This is a difficult exercise. In general, a developer should be able to reason on different levels of abstraction, more or less at the same time. Again, if you’re aware of it, your experience will help you here, and you can also push yourself to think of all the possible risks and consequences at different levels.

If you work in a team, try to participate (at least a bit) into the implementation of the solution. It’s not good to create silos in teams (that is, only a couple of members have some information others don’t have).

You can go as far as looking at other projects, and ask yourselves these questions:

• Did we had similar problems on these other projects? How did we solve them?
• What was the context of these projects? Is it similar to our current context?
• What did we learn from these other problems, and their implementation? Is the implementation similar to what we’re doing now?

In any case, I would definitely recommend you to write a development journal. I write mine for years, and it has been valuable in many cases. I basically write in there:

• The interesting problems I had.
• The decisions made.
• How the implementation of the solution evolved overtime.
• The possible mistakes we made along the way.

It’s a great resource when you have a problem and you want to look at your past experience.

To evaluate your decisions overtime, nothing will beat a good monitoring process: logs, tests, and so on. It’s what the book Building Evolutionary Architecture call “fitness functions” for example, some monitoring allowing you to measure how healthy your architecture stays overtime. It doesn’t have to stop to the architecture; you can think about different monitoring system to see how something evolve, especially if the solution has still a lot of uncertainty regarding its benefits, drawbacks, and risks.

You can also do that retrospectively: looking at how the code complexity evolve overtime using Git for example.

Retrospective on the Process

We defined the problem, implemented a solution iteratively, and now the problem is gone. That’s it! We made it! Are we done now?

Decisions are sometimes not optimal, and implementing a solution successfully doesn’t mean that there wasn’t a better (simpler) one to begin with. That’s why it can be beneficial to look back and understand what went right, and what went wrong. For example, we can ask ourselves these questions:

• Looking at what we learned during the whole process, is there a potentially better hypothesis to solve the problem in a simpler, more robust way?
• What are the benefits and drawbacks we missed when speaking about the different hypothesizes, but we discovered during the implementation? Why we didn’t think about them beforehand?
• What other problems did we encounter during the implementation? Did we solve them? Did we differ some? What should be the next steps regarding these new problems?
• What kind of monitoring did we put in place to make sure that the solution won’t have undesired outcomes overtime? Can we learn something with this data?

Reflecting on past solutions is a difficult thing to do. There is no way to logically assess that the decision taken was better than others, since we didn’t implement the other hypothesizes, and we didn’t look at them overtime to appreciate their consequences. But you can still look at the implementation of the solution overtime, and write in your developer journal each time there is a bug which seems directly related to the solution. Would the bugs be the same if another solution would had been applied?

Bugs are often not an option; they will pop up, eventually. Nonetheless, it’s important to make sure that you can fix them in a reasonable amount of time, and that you don’t see them creeping back in the codebase after being solved. Some metrics, from the DevOps movement (like MTTR for example) can help here. Sometimes, bugs will show you a better, more refined solution to the original problem; after all, bugs can also give you some useful information. They are also the most direct result of the implementation of your solution.

If you want to know more about measuring complexity (which can be also used to measure complexity overtime after applying a solution), I wrote a couple of articles on the subject .

Humility in Problem-Solving

It’s time to do a little summary. What did we see in this article?

• We need to ensure that the problem we found is really a problem we need to solve. Is there any value to solve the problem? Is it even a problem?
• Try to determine what kind of problem you have: a problem which can have multiple, specific, known answers (like a technical problem), or a problem which depends on the real-life context, without known solutions?
• Defining the problem is important. Try to define it using different words. Write these definitions down. Does everybody in your team understand the problem equally?
• It’s time to explore the solution space. Draft a couple of hypothesizes, their benefits, drawbacks, and risks. You can also do some prototyping if you think it would give you more information to take the best decision.
• Do you have enough information to implement a hypothesis, becoming effectively a solution? If it’s not the case, it might be better to keep the status quo and try to solve the problem later, when you’ll have more information. But don’t forget the problem!
• If you decide to implement a solution, do it step by step, especially if you’re unsure about the consequences of your decisions. Implement an independent part of the hypothesis, look at the consequences, adjust if necessary, and re-iterate.
• When the solution is implemented, it’s time to reflect on the whole process: did we solve the problem? What other problems did we encounter? Maybe another solution would have been better? Why?

As I was writing above, most problems you’ll encounter will be complex ones, embedded into a changing environment with different moving parts. As a result, it’s difficult to train to solve problems in a vacuum; the only good training I know is solving real life problems. That’s why your experience is so important.

Experience build your intuition, which in turn increase your expertise.

You’ll never have 100% certainty that a solution will bring you the desired outcome, especially if you are in front of a complex problem with a blurry context. If you are absolutely convinced that you have the good solution without even beginning to implement it, I’d advise you to stay humber in front of the Gods of Complexity, or they will show you how little you know.

• How to solve it
• Hammock Driven Development
• When Deferring Decisions Leads to Better Codebases
• Lean Development - deferring decision

• Online Degree Explore Bachelor’s & Master’s degrees
• MasterTrack™ Earn credit towards a Master’s degree
• University Certificates Advance your career with graduate-level learning
• Top Courses
• Join for Free

Computational Thinking for Problem Solving

Taught in English

Some content may not be translated

Financial aid available

113,773 already enrolled

Gain insight into a topic and learn the fundamentals

Instructor: Susan Davidson

Included with Coursera Plus

(1,359 reviews)

Skills you'll gain

• Simple Algorithm
• Python Programming
• Problem Solving
• Computation

Details to know

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 4 modules in this course

Computational thinking is the process of approaching a problem in a systematic manner and creating and expressing a solution such that it can be carried out by a computer. But you don't need to be a computer scientist to think like a computer scientist! In fact, we encourage students from any field of study to take this course. Many quantitative and data-centric problems can be solved using computational thinking and an understanding of computational thinking will give you a foundation for solving problems that have real-world, social impact.

In this course, you will learn about the pillars of computational thinking, how computer scientists develop and analyze algorithms, and how solutions can be realized on a computer using the Python programming language. By the end of the course, you will be able to develop an algorithm and express it to the computer by writing a simple Python program. This course will introduce you to people from diverse professions who use computational thinking to solve problems. You will engage with a unique community of analytical thinkers and be encouraged to consider how you can make a positive social impact through computational thinking.

Pillars of Computational Thinking

Computational thinking is an approach to solving problems using concepts and ideas from computer science, and expressing solutions to those problems so that they can be run on a computer. As computing becomes more and more prevalent in all aspects of modern society -- not just in software development and engineering, but in business, the humanities, and even everyday life -- understanding how to use computational thinking to solve real-world problems is a key skill in the 21st century. Computational thinking is built on four pillars: decomposition, pattern recognition, data representation and abstraction, and algorithms. This module introduces you to the four pillars of computational thinking and shows how they can be applied as part of the problem solving process.

What's included

6 videos 4 quizzes 2 peer reviews 4 discussion prompts

6 videos • Total 44 minutes

• 1.1 Introduction • 4 minutes • Preview module
• 1.2 Decomposition • 6 minutes
• 1.3 Pattern Recognition • 5 minutes
• 1.4 Data Representation and Abstraction • 7 minutes
• 1.5 Algorithms • 8 minutes
• 1.6 Case Studies • 11 minutes

4 quizzes • Total 50 minutes

• 1.2 Decomposition • 10 minutes
• 1.3 Pattern Recognition • 10 minutes
• 1.4 Data Representation and Abstraction • 15 minutes
• 1.5 Algorithms • 15 minutes

2 peer reviews • Total 60 minutes

• Applying Computational Thinking in Your Life • 30 minutes
• Project Part 1: Applying the Pillars of Computational Thinking • 30 minutes

4 discussion prompts • Total 40 minutes

• Applying Decomposition in Your Life • 10 minutes
• Applying Pattern Recognition in Your Life • 10 minutes
• Applying Data Representation and Abstraction in Your Life • 10 minutes
• Applying Algorithms in Your Life • 10 minutes

Expressing and Analyzing Algorithms

When we use computational thinking to solve a problem, what we’re really doing is developing an algorithm: a step-by-step series of instructions. Whether it’s a small task like scheduling meetings, or a large task like mapping the planet, the ability to develop and describe algorithms is crucial to the problem-solving process based on computational thinking. This module will introduce you to some common algorithms, as well as some general approaches to developing algorithms yourself. These approaches will be useful when you're looking not just for any answer to a problem, but the best answer. After completing this module, you will be able to evaluate an algorithm and analyze how its performance is affected by the size of the input so that you can choose the best algorithm for the problem you’re trying to solve.

7 videos 6 quizzes 4 peer reviews

7 videos • Total 68 minutes

• 2.1 Finding the Largest Value • 8 minutes • Preview module
• 2.2 Linear Search • 5 minutes
• 2.3 Algorithmic Complexity • 8 minutes
• 2.4 Binary Search • 11 minutes
• 2.5 Brute Force Algorithms • 13 minutes
• 2.6 Greedy Algorithms • 9 minutes
• 2.7 Case Studies • 12 minutes

6 quizzes • Total 65 minutes

• 2.1 Finding the Largest Value • 10 minutes
• 2.2 Linear Search • 10 minutes
• 2.3 Algorithmic Complexity • 10 minutes
• 2.4 Binary Search • 10 minutes
• 2.5 Brute Force Algorithms • 15 minutes
• 2.6 Greedy Algorithms • 10 minutes

4 peer reviews • Total 115 minutes

• Finding Minimum Values • 30 minutes
• Binary Search • 30 minutes
• Greedy vs. Brute Force Algorithms • 30 minutes
• Project Part 2: Describing Algorithms Using a Flowchart • 25 minutes

Fundamental Operations of a Modern Computer

Computational thinking is a problem-solving process in which the last step is expressing the solution so that it can be executed on a computer. However, before we are able to write a program to implement an algorithm, we must understand what the computer is capable of doing -- in particular, how it executes instructions and how it uses data. This module describes the inner workings of a modern computer and its fundamental operations. Then it introduces you to a way of expressing algorithms known as pseudocode, which will help you implement your solution using a programming language.

6 videos 5 quizzes 5 peer reviews

6 videos • Total 45 minutes

• 3.1 A History of the Computer • 7 minutes • Preview module
• 3.2 Intro to the von Neumann Architecture • 8 minutes
• 3.3 von Neumann Architecture Data • 6 minutes
• 3.4 von Neumann Architecture Control Flow • 5 minutes
• 3.5 Expressing Algorithms in Pseudocode • 8 minutes
• 3.6 Case Studies • 10 minutes

5 quizzes • Total 50 minutes

• 3.1 A History of the Computer • 10 minutes
• 3.2 Intro to the von Neumann Architecture • 10 minutes
• 3.3 von Neumann Architecture Data • 10 minutes
• 3.4 von Neumann Architecture Control Flow • 10 minutes
• 3.5 Expressing Algorithms in Pseudocode • 10 minutes

5 peer reviews • Total 120 minutes

• (Optional) von Neumann Architecture Control Instructions • 20 minutes
• (Optional) Understanding Pseudocode • 20 minutes
• von Neumann Architecture Data & Instructions • 30 minutes
• Reading & Writing Pseudocode • 25 minutes
• Project Part 3: Writing Pseudocode • 25 minutes

Applied Computational Thinking Using Python

Writing a program is the last step of the computational thinking process. It’s the act of expressing an algorithm using a syntax that the computer can understand. This module introduces you to the Python programming language and its core features. Even if you have never written a program before -- or never even considered it -- after completing this module, you will be able to write simple Python programs that allow you to express your algorithms to a computer as part of a problem-solving process based on computational thinking.

9 videos 12 readings 12 quizzes

9 videos • Total 90 minutes

• 4.1 Introduction to Python • 6 minutes • Preview module
• 4.2 Variables • 13 minutes
• 4.3 Conditional Statements • 8 minutes
• 4.4 Lists • 7 minutes
• 4.5 Iteration • 14 minutes
• 4.6 Functions • 10 minutes
• 4.7 Classes and Objects • 9 minutes
• 4.8 Case Studies • 11 minutes
• 4.9 Course Conclusion • 8 minutes

12 readings • Total 125 minutes

• Programming on the Coursera Platform • 10 minutes
• Python Playground • 0 minutes
• Variables Programming Activity • 20 minutes
• Solution to Variables Programming Activity • 10 minutes
• Conditionals Programming Activity • 20 minutes
• Solution to Conditionals Programming Activity • 10 minutes
• Solution to Lists Programming Assignment • 5 minutes
• Solution to Loops Programming Assignment • 10 minutes
• Solution to Functions Programming Assignment • 10 minutes
• Solution to Challenge Programming Assignment • 10 minutes
• Solution to Classes and Objects Programming Assignment • 10 minutes
• Solution to Project Part 4 • 10 minutes

12 quizzes • Total 185 minutes

• (Optional) Challenge Programming Assignment • 20 minutes
• 4.2 Variables • 10 minutes
• 4.3 Conditional Statements • 5 minutes
• 4.4 Lists • 10 minutes
• Lists Programming Assignment • 15 minutes
• 4.5 Iteration • 10 minutes
• Loops Programming Assignment • 30 minutes
• Functions Programming Assignment • 20 minutes
• 4.7 Classes and Objects • 10 minutes
• Classes and Objects Programming Assignment • 20 minutes
• Project Part 4: Implementing the Solution in Python • 25 minutes

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.

Recommended if you're interested in Algorithms

University of Michigan

Problem Solving Using Computational Thinking

Google Cloud

Modernize Infrastructure and Applications with GC - Español

Politecnico di Milano

Fundamentals of Organization

Dartmouth College

Neural Basis of Imagination, Free Will, and Morality

Why people choose coursera for their career.

Learner reviews

Showing 3 of 1359

1,359 reviews

Reviewed on Feb 15, 2019

Thoroughly enjoyed the course. If you are new to computer science or need a refresher of the basic concepts you learned in high school / college, this is the perfect course

Reviewed on Aug 4, 2022

Informative but the last assignment on Week 4 is misleading in that you can only submit the search function but the instructions say that you can test your code and the program will test it as well.

Reviewed on Jan 18, 2019

Useful course taught at an adequate rate. I recommend it for people who are interested in learning the basics of computational thinking, i.e. a systematic approach to problem-solving.

New to Algorithms? Start here.

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions

Do i need to know how to program or have studied computer science in order to take this course.

No, definitely not! This course is intended for anyone who has an interest in approaching problems more systematically, developing more efficient solutions, and understanding how computers can be used in the problem solving process. No prior computer science or programming experience is required.

How much math do I need to know to take this course?

Some parts of the course assume familiarity with basic algebra, trigonometry, mathematical functions, exponents, and logarithms. If you don’t remember those concepts or never learned them, don’t worry! As long as you’re comfortable with multiplication, you should still be able to follow along. For everything else, we’ll provide links to references that you can use as a refresher or as supplemental material.

Does this course prepare me for the Master of Computer and Information Technology (MCIT) degree program at the University of Pennsylvania?

This course will help you discover whether you have an aptitude for computational thinking and give you some beginner-level experience with online learning. In this course you will learn several introductory concepts from MCIT instructors produced by the same team that brought the MCIT degree online.

If you have a bachelor's degree and are interested in learning more about computational thinking, we encourage you to apply to MCIT On-campus (http://www.cis.upenn.edu/prospective-students/graduate/mcit.php) or MCIT Online (https://www.coursera.org/degrees/mcit-penn). Please mention that you have completed this course in the application.

Where can I find more information about the Master of Computer and Information Technology (MCIT) degree program at the University of Pennsylvania?

Use these links to learn more about MCIT:

MCIT On-campus: http://www.cis.upenn.edu/prospective-students/graduate/mcit.php

MCIT Online: https://www.coursera.org/degrees/mcit-penn

When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

What is the refund policy?

You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy Opens in a new tab .

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions

Problem-Solving Skills for Software Developers: Why & How to Improve

Problem-solving skills go hand-in-hand with software development. Learn some great problem-solving techniques and tips for improvement here!

Software developer jobs today require that you possess excellent problem-solving skills , and for good reason. Unfortunately, there seems to be a sort of talent gap when it comes to this one skill required of all software developers.

Troubleshooting and problem resolution are both informally and formally taught, but you mostly find that software developers have to learn problem-solving skills on their own. This is true for self-taught developers , obviously, but also even for those with software engineering degrees or who’ve graduated from coding boot camps.

This is why it’s necessary to acquaint yourself with the problem-solving process, whether you are a newbie or an experienced developer. In this article, we’ll explore everything you need to know about problem-solving so you can 10x your software development career.

What are Problem-Solving Skills?

As a developer, what do we mean by problem-solving? Let’s attempt a simple definition.

In software development, problem-solving is the process of using theories and research to find solutions to a problem domain, while testing different ideas and applying best practices to achieve a desired result. Problem-solving also has to do with utilizing creativity and logical thought processes to identify problems and resolve them with software.

Becoming a great software developer hinges more on learning algorithms than programming languages or frameworks . And algorithms are simply step-by-step instructions to solve a given problem.

Read More : How to Build a Software Engineer Portfolio (With Examples & Tips)

Why are impeccable problem-solving skills crucial?

Making good use of a computer language can be likened to being a skilled writer. An effective writer must know how to construct sentences and use grammar appropriately. There’s more to writing than just knowing all the words in the dictionary, and that’s how it works for developers, too.

You have different tasks to work on as a software developer, including perhaps designing, coding, and troubleshooting. Much of your time will be spent on identifying problems, spotting and correcting bugs, and making sense of codebases from before you started working there. Being ingenious at problem-solving is essential in creating incredible solutions to issues that arise throughout software development.

To demonstrate ingenuity, let’s consider Google’s autocomplete tool as an example.

The autocomplete tool is built to suggest related terms in the search bar as you type. The idea behind the tool is to reduce more than 200 years of time spent typing daily and to help users save time by up to 25% while typing.

Here’s what had to be done:

• To activate real-time completion of suggestions, the UI experience and JavaScript had to be implemented.
• Next, since users could type just about anything, the autocomplete suggestions had to be compiled into a sensible list dependent on user input.
• Then, Google had to create a back-end sustainability system for this function. Doing this meant massively increasing its infrastructure to accommodate all forms of data query and HTTP requests.
• Finally, the user interface had to be refined by software engineers in order to make sure that every user enjoyed a worthwhile experience. So they employed Google Trends to power the auto-completion tool while using algorithms to take out explicit or offensive predictions in line with Google’s auto-completion policy.

This is just one of Google’s innumerable problem-solving examples, but it’s clear to see that solving problems involves more than just telling a computer to do stuff. It’s about your ability to come up with parameters rightly tailored to target users so they can meet their goals.

So why must developers focus on problem-solving at work?

Software developers work with a wide range of people and departments, and it’s common to discover that some clients and teams find it difficult to define what they want. As a problem solver, it’s up to you to help them identify their needs and communicate their thoughts in an effective way.

Of course, you’ll need time and practice to develop your problem resolution ability. That’s because it’s less about solving problems faster but more about coming up with the best solution . And then you’ll need to deploy that solution.

Read More : Common Interview Questions for Software Developer Jobs (Non-Technical)

Types of problem-solving skills

Now let’s talk about four types of problem-solving skills for developers:

1.  Parallel thinking

As a software developer, parallel thinking is a crucial skill necessary to perform optimally. This makes it possible for you to carry out two tasks that complement each other at the same time (like an optimized form of multitasking skills). Being able to reorder tasks to boost parallel execution can help to improve your output and save valuable time .

2. Dissecting broad and/or complex goals

When it comes to building software, you will need to effectively outline the steps and tasks necessary to achieve your goal. Developers must learn to break large and complex tasks into smaller assignments because this is an important skill that will help you create results with precision.

3. Reimplementing existing solutions

You don’t always need to reinvent the wheel. Part of being an effective software developer comes with being able to use already existing tools before even thinking of creating new solutions. Developing problem-solving skills is very much connected to finding solutions that already exist and reusing them.

4. Abstraction

Keep in mind that goals tend to evolve. So if your client comes up with new ideas, that will mean changing your design goals and reordering your tasks. A good programmer must learn to create solutions in such a way that does not require a complete redesign from scratch.

You also have to become adept at abstracting problems so that your solutions can get them resolved so long as they aren’t entirely different from the original issue. You don’t necessarily have to abstract every aspect to avoid more complications being created. This calls for balance by abstracting only where necessary without making narrow decisions.

Read More : Learn 8 Great Benefits of Working From Home

4 Important Tips & Strategies for Improving Problem-Solving Skills

To keep your problem-solving skills and techniques from growing weaker over time, you need to exercise them non-stop. As they say: practice makes perfect!

To train the problem-solving side of your brain, these four tips and strategies can help you improve your abilities:

1. Make problem-solving a part of your life

Never restrict yourself to working on problems only during work hours. Don’t make it a chore, but, instead, do things that make problem-solving look fun. The game of chess, solving puzzles, and playing video games that compel you to think critically will help strengthen your problem-solving skills, and you can tell your significant other you are advancing your career! 🙂

When you come to a complex problem in your life, whether it’s budgeting for a home or renovating the downstairs bathroom, approach it both creatively and critically. Ask yourself: What would a great software engineer do in this situation?

2. Use different platforms to solve problems

Proffer solutions to a set of problems without restricting yourself to one platform. Using different platforms and tools regularly helps make sure you become flexible as a problem-solver. And it makes sense, because there really is no universal solution for the different problems that pop up in your line of work. Trying out different platforms to solve different problems helps you to keep an open mind and enables you to test out different techniques when looking to find solutions.

Read More : 12 Common Mistakes Keeping You From Landing Your First Developer Job

3. Be open to assistance from external sources

Part of being a good software developer comes with being able to ask for help and also accept all forms of feedback. You might need a different opinion or a new set of eyes to help find the most fitting solution to some problems. It makes sense to view building problem-solving skills as more of a team effort rather than a personal journey.

Have an open mind and heart to function not only as an individual but also as a collective. It’s a utopian working environment where everyone supports each other to become better versions of themselves. So if you come across an issue that keeps you stuck, get help! You may find someone who has a more refined framework or method you never knew existed or would have thought of using. You could then learn from them and add their solution to your toolkit.

Get feedback often, as well. This could be the catalyst to making improvements to your processes and evolving them into something truly refined.

4. Tackle new problems using lessons from past solutions

As you practice and finesse your ability to identify problems and find solutions, you’ll begin to notice patterns. It’s more like developing your toolbox armed with a wide range of solutions that have proved useful in the past. So when problems emerge, you will notice how easy it is to take some of those old solutions and apply them to the new problem.

The more you attempt to apply creativity in solving problems, the more you grow your skills. In the long run, that will help you find the right solutions faster and apply them to a wide range of problems more naturally. It’s all about improving the effectiveness and efficiency with which you tackle new problems while applying only the best possible solutions.

Read More : How to Stay Motivated at Work

3 Complementary Skills to Improve to Become a Good Problem Solver

Developing software is mostly about problem-solving at the very core before even writing your first lines of code. You have to identify problems that can be solved using software. Then you have to go on to understand how people try to solve such problems in real life.

It’s up to you to come up with a framework that allows you to take both the problem and the solution and convert them into computer code. And you have to do this in such a way that makes the software even more efficient and effective than a human.

While going through this process, developers also have to handle other problems such as deadline deliveries, checking for bugs and fixing them, and collaborate across teams. So, supporting skills must not be overlooked.

Software developers must build interpersonal skills and collaboration skills . Being able to empathize, accept feedback, handle criticism, listen intently, and show respect for others are all important characteristics and abilities necessary for teamwork, and, thus, necessary for solving problems on the job.

Read More : 5 Ways to Stand Out & Get Noticed in Your Current Development Job

Communication

No one is an island, and that’s true when you consider how software engineers work. Building software requires keeping up with clients and teammates and other departments. You can’t afford to be a Lone Ranger, at least not 100% of the time, and that’s why employers always look for good communication skills.

Being a good software developer also involves how well you can break down very complex concepts to laypeople. You want to be the kind of person who fixes a problem and is able to explain how you were able to do it. It’s all about your ability to be clear and articulate about every aspect of your work. And you want to be able to communicate not just verbally but also in written form.

To build your communication skills as a developer, you can learn from more experienced people and observe how they interact with their clients. And, don’t forget, with more and more companies becoming global enterprises and going remote, it’s important to brush up on your intercultural communication skills , as well.

Logical thinking

The difference between elite software developers and average ones is often said to be logical thinking. The ability to process thoughts logically is important, because you’ll often spend most of your time finding and fixing bugs rather than writing code.

Problems can show up from just about anywhere, even from what seems to be the most insignificant errors. So, your ability to detect software issues and solve these problems using deductive thought processes is a vital ingredient to your success as a software developer.

Read More : Questions to Ask at Interviews for Software Engineering Jobs

Problem-Solving Stages & Practices

There are countless problem-solving processes and various schools of thought regarding the best way to approach problems whenever they arise. To solve that problem, we’ve pooled some of these frameworks together to come up with a comprehensive approach to problem-solving.

Step 1 – Define the problem

You have to first start with problem identification. Knowing what you are dealing with is important, because you don’t want to risk spending valuable time applying wrong solutions. Avoid making automatic assumptions. Even when the symptoms look familiar, you want to investigate properly because such signs could be pointing to something else entirely.

Problems in software development come in different sizes and scopes. You could be having trouble getting some aspects of the product to respond in the desired way. Or maybe you’re having issues trying to decipher a codebase section where you can no longer communicate with the original developers. Sometimes, the problem could come in the form of an unfamiliar error message and you’re at loss.

Once you’re able to define the problem, make sure to document it.

Step 2 – Analyze the problem

Now it’s time to carry out problem analysis . Before deciding what problem resolution methods to adopt, it’s necessary to find out all there is to the issue, which builds on our first step. This will make it easier to come up with ideas and solutions later on.

Problem analysis isn’t always a walk in the park. There are times when the problem involves a very small mistake such as failing to import a package correctly or a small syntax error. Other times, however, it could be such a huge error, like the entire program acting differently than what you want. There might be no alarms or blinking red lights to tell you what the exact problem is.

If you encounter such situations, you can find answers by articulating the problem. Document what you intend to do, what you’ve done, the original intention for the program, and where you currently are. Communication comes in handy here, of course, not just in your documentation, but also in how you relay it to your teammates.

Read More : Got a Busy Developer Schedule? Here’s How to Keep Learning & Make Time

Step 3 – Brainstorm

This step has to do with generating ideas, and you can benefit from discussing the problem with a team and then coming up with ways to get it fixed. Keep in mind that problem-solving at work involves interacting with a diverse group of people where the individuals have unique skill sets and experiences.

Many developers tend to neglect the previous steps and rush straight into brainstorming. That’s definitely not a good way to go about problem-solving. The idea is not to skip the important steps in the process.

Once you get to the point where ideas need to be generated, do not discard any, because this step relies on a wide range of ideas. Only after gathering as many perspectives as possible should you then begin reviewing and narrowing down to the best possible solution.

Step 4 – Make a decision

At this point, all viable solutions have to be analyzed before selecting the most appropriate one to implement. Picking the best possible solution depends on its ability to meet certain criteria. It must be suitable, feasible, and then acceptable.

What it means is that the solution must be able to get the problem solved. It should also be easy to see how such a solution fits into the equation. And then every member of the team involved in the brainstorming process has to unanimously accept the solution.

Read More : How to Network as a Software Engineer

Step 5 – Implement

After identifying and choosing the solution, the next logical step is to plan out the implementation process and then execute it. Coming up with a detailed plan is crucial if the solution is to be a success.

Now this plan must detail all the necessary steps required to implement the solution. It will also explain the length of time and stages of work required. Once all of that is put in place, you can then move forward with the execution. The idea is not just to execute a solution but to do it the right way.

Implementation using automated tests can help to keep unexpected issues from arising in the future. Some other problem-solving practices or approaches begin the process with this step. So, whenever any changes are made to the project, tests asserting that the changes will perform as required will be written first before the changes are then made.

Step 6 – Evaluate

No problem-solving process can be deemed comprehensive enough if there is no room for evaluation. Whatever the solution may be, it has to undergo strict evaluation in order to see how it performs. That will also help determine whether the problem still exists and the extent to which such an issue keeps recurring.

In the event that the problem persists despite the implementation of a detailed plan, then the developer and team may even have to restart the problem-solving process. However discouraging that may sound, at least you’ll have caught it early enough. And, this also proves the process worked.

Read More : How to Become a Software Engineer: Education, Steps & Tips for Success

Final Thoughts

Developing problem-solving skills is quite necessary for software developers. To be a successful problem solver, you will need lots of years down the line to practice what you study.

Always remember that you are a problem solver first before anything else. There is more to building software than just understanding the tech behind it and writing lines of code. It’s all about improving your ability to identify problems and find solutions, and that will need lots of experience on your part.

Never shy away from problems, but learn to think critically and logically in any situation. By applying the six-step strategy for problem-solving at work discussed in this piece, you will be more equipped to come up with the most effective and efficient solutions.

We hope you enjoyed reading our guide on how to solve a problem as a software developer and ways to improve skills as a problem solver! If you have any questions, feedback, or other great problem-solving techniques or methods, let us know in the comments below 🙂

The Arc team publishes insightful articles and thought leadership pieces related to software engineering careers and remote work. From helping entry-level developers land their first junior role to assisting remote workers struggling with working from home to guiding mid-level programmers as they seek a leadership position, Arc covers it all and more!

Further reading

Ready to Take On a Senior Role or Leadership Position as a Developer?

Here Are 43 of the Best Online Developer Communities to Join in 2024

Key Analytical Skills for Developers (& How to Continually Improve Them)

Here’s When You Can TRULY Call Yourself a “Senior” Software Developer

Time Management Skills for Developers: Best Tips, Tools, and Strategies

Software Engineer Degree: Pros, Cons & Alternatives

DEV Community

Posted on Aug 10, 2022

How to develop strong problem solving skills as a software developer

Introduction.

It is generally known that problem solving is an essential skill for software engineers.

Good problem solving skills involve being able to think creatively and analytically, breaking down problems into smaller parts and using a systematic approach to find solutions. Strong problem solving skills are essential for a successful career in software development. In this article we will review some approach.

Various Methods

Trial and error method.

The trial and error method is a common problem-solving technique in which potential solutions are tried out one by one until a working solution is found. This method can be used for both simple and complex problems.

Divide and conquer

Another approach is to use a more systematic method, such as divide and conquer or reduction. Divide and conquer is a software engineering technique for solving complex problems by breaking them down into smaller, more manageable pieces. This allows for more efficient and effective problem solving by breaking down a complex problem into smaller, more manageable sub-problems. Once these sub-problems have been solved, they can be combined to solve the larger, more complex problem.

One common example of divide and conquer is the use of recursion. Recursion involves breaking a problem down into smaller sub-problems, solving each sub-problem, and then combining the solutions to the sub-problems to solve the larger problem. Another common example is the use of algorithms, such as the quick sort algorithm, which break a problem down into smaller pieces, solving each piece, and then combining the solutions to the pieces to solve the larger problem.

Once a solution is found, it is important to learn from the experience and use that knowledge to improve future problem solving skills. This includes understanding what went wrong, what could have been done better, and how similar problems can be avoided in the future. By taking these steps, software developers can become more effective problem solvers.

Problem solving skills is important in its own way. As a software developer, you should try to develop all of these skills in order to be successful.

Analytical skills:

Analytical skills are the ability to collect and analyze data, identify patterns and trends, and make decisions based on that information. They involve both logical and creative thinking, as well as the ability to pay attention to detail. Strong analytical skills are important in many different fields. Some examples:

• Being able to break down a problem and identify the various components
• Being able to identify patterns and trends
• Being able to see relationships between different pieces of data
• Being able to make decisions based on data
• Being able to solve complex problems

Creative thinking

Creative thinking in computer science is all about coming up with new and innovative ways to solve problems. It’s about thinking outside the box and coming up with creative solutions that nobody has thought of before.

It’s important to be creative in computer science because it’s a constantly evolving field. If you’re not constantly coming up with new ideas, you’re going to fall behind. Creative thinking is what keeps computer science moving forward.

If you want to be successful in computer science, you need to be creative. It’s not enough to just learn the basics. You need to be constantly thinking of new and better ways to do things. So if you’re not a naturally creative person, don’t worry. Just keep working at it and you’ll get there.

Logical reasoning

Logical reasoning is a process of making deductions based on given information. In computer science, this process is often used to solve problems and to create new algorithms. To reason logically, one must first identify the premises and then use them to reach a valid conclusion.

Practice is one of the best ways to improve your problem solving skills. You can do this by working on coding challenges, participating in online coding contests, or simply trying to solve problems you encounter in your daytoday work. Collaboration is another great way to improve your problem solving skills. When you work with others, you can learn from their experiences and share your own insights. This can help you develop a more well rounded approach to problem solving.

If you're a software developer, congratulations! You have chosen one of the most mentally demanding professions there is. And if you want to be successful, you need to have strong problem solving skills.

My last tip: get comfortable with being stuck! It's normal to feel stuck when you're trying to solve a problem and don't be afraid to ask for help. We all need help from time to time, and there's no shame in admitting that you need help.

So there you have it! Follow these tips and you'll be well on your way to developing strong problem solving skills as a software developer.

Like this article? Join the discussion in our Discord channel .

Top comments (26)

Templates let you quickly answer FAQs or store snippets for re-use.

• Work Lead Dev
• Joined Jul 12, 2022

For students , don't underestimate the mathematics. You definitively don't need a master degrees in maths to be a successfull developer BUT , mathematics train your brain to think. All this theorems you probably never used in real world , all thoses equation you resolved in high school helped your logical reasoning.

Finally, to get better at resolving problem you definitely should resolve problem :P The more you resolve , the better you get !

• Joined Mar 14, 2022

I agree with you! I mentioned it, practice and practice.. About maths it is also another alternative

• Joined Sep 21, 2021

Science shows that there really is no knowledge transfer between unrelated fields. There is essentially no way to get overall better at thinking. You either can relate what you know already to the thing or you can't. In first case it's experience, not brain trained to thinking, in second it depends on if you learned how to learn.

Logical reasoning is a small part that everyone does even without thinking, but it doesn't transfer if you don't use abstractions to relate what you don't know with what you do know.

You can get better at resolving problems overall, but it's a tiny improvement if you don't focus on learning how to solve problems. Solving problems on it's own gives you experience you can use at solving similar things. But it won't help with different problems.

That being said if you can relate math you learned to problems you need to solve it can sometimes make something impossible into something very easy. That on it's own is a good reason to get good at maths. The other one is training on how to read information dense domain texts.

• Location Yenagoa, Nigeria
• Joined Feb 1, 2021

You're right, solving mathematical problems help increase logical reasoning, and I believe that's a major reason we do maths in Computer Science. Working with numbers is a top-tier ability if you want to become a successful "software engineer". Thanks.

You're right, solving mathematical problems help increase logical reasoning, and I believe that's a major reason we do maths in Computer Science. Working with numbers is a top-tier ability if you want to become a successful "software engineer". Thanks

• Location India
• Joined Aug 17, 2019

Just like Neil deGrasse Tyson said, It is not about finding the value of x, It is about process for finding the x.

• Location Munich, Germany
• Work Software Development Team Lead
• Joined Oct 4, 2019

I have to disagree, especially on the Maths->Logics path. I found it's a lot easier to go into Logics without Maths, even though the basic principles are similar (since both are just formal languages).

• Education Computer Science
• Work Senior Software Developer at Syskron GmbH
• Joined Jan 30, 2021

What he meant is that math trains your brain. Either way it’s math or not it’s necessary to think structured to solve programming problems. I have seen so many bad code written my mathematics (no offense 😃)

• Email [email protected]
• Location Living in the path between home and office
• Education The school of life and entry points
• Work 📖 Researching and learning everything about API Security and Business Logic
• Joined Jun 22, 2022

• Location Nashville
• Education Bootcamp Grad
• Work Fullstack Developer
• Joined Mar 5, 2017

You have to joke so you don't cry.

• Email [email protected]
• Location Budapest, Hungary
• Education engineering
• Work software developer @ itemis
• Joined Dec 25, 2021

what a cool drawing!

Hahaha @apimike humor is important!

I just said that math was a way , and because it's basically taught in every school of the world (contrary to other knowledge) it's important to embrace it and understand that what you are taught is not how to multiply 2 number but actually how to think.

I realized it way too late, I always hated math when I was young because I wasn't able to figure out the point of what I was taught.

The misconception is , that you have to be good at math to be a good developer. Indeed that completely false (unless your are developing for some specific field).

• Joined Nov 24, 2020

Being a successful software developer, one needs to be open to learning and unlearning. I've learnt and unlearnt and am grateful I did. Most of the time I share with colleagues and friends, I got to find out the knowledge that was most neglected, tends to be a challenge for someone else.

It's a mentally demanding field. It's not just a job, it's a way of life.

• Location Scotland, United Kingdom
• Joined Nov 13, 2020

this is spot on

Thanks for the article, it resonates.

I read most of the comments and can't stay quiet. From my years of experience, math can be a really powerful tool when it comes to solving problems. Ofcourse is only one of the tools out there that can empower a programmer. Another tool I found to be crucial are flow charts. Being able to construct them the right way can help a lot.

e. g. This problem I solved it using only math. And I have used many concepts of math and physics for game development. To understand algebra, arrays, matrixes, vectors, magnituds, forces, inertia, acceleration, etc... Can be crucial on the development of certain softwares. But it all depends the area in which you are coding.

• Location Dhaka, Bangladesh
• Education Dep. of Computer Science & Engineering(CSE), BAIUST
• Work Unemployed

A newbie here. I've a different problem . i.e. I face difficulty while implementing the code but I know the theory & logic behind it. Any suggestion/advice for me anyone ?

Hiiii maybe I will write an article about it ! Nice idea :)

• Email [email protected]
• Joined Apr 23, 2019

a great read

• Joined Jul 9, 2020

I wonder if there is literature about how to improve and train your problem solving skills. Could you recommend books about this topic?

Personnaly I don't know books on this topic, but if you have got something share it with us :)

• Joined Mar 11, 2022

this article and that comments below are good

• Email [email protected]
• Location Philippines
• Education Javascript Development with Clever Programmer
• Work Software Consultant at Servio Australia
• Joined Aug 29, 2020

Thank you. "Divide and conquer" works for me.

• Location Rwanda
• Joined Jul 30, 2022

I think, being open-mind is a way to simulate our brain when it comes to creative thinking. btw nice article

• Joined Sep 17, 2021

I have a short, simple rule: be creating and imaginary as much as possible. That's to say whatever others do you can do differently and/or more crazily.

• Joined Mar 13, 2023

Software quality assurance consulting services are specialized services offered by qualified experts who evaluate and examine software systems for flaws, weaknesses and potential threats. These qa company haberforever.com/ help companies improve the quality and reliability of their software products by providing extensive knowledge and skills in software testing methodology, automation technology and industry best practices.

Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink .

Hide child comments as well

For further actions, you may consider blocking this person and/or reporting abuse

Supabase Security Advisor & Performance Advisor

Yuri - Apr 19

Tools & Extensions for Vue JS

Chabba Saad - Apr 22

Building Linux Commands From Scratch 🐧 clear Command

Suraj Kareppagol - Mar 30

Supercharging Rails: Enabling Ruby's YJIT Compiler in Production

Colin Soleim - Apr 22

We're a place where coders share, stay up-to-date and grow their careers.

• Penn Engineering Online Degrees
• Penn Engineering Online Dual Dual Degree
• Online Graduate Certificates
• Take a Course
• On-Demand Learning
• Computer Science Essentials for Software Development
• Lifelong Learning
• Credentials

Gain the systematic knowledge you need to be a software developer. In this four-course certificate program, you’ll learn the core principles of professional software development—from the fundamentals of object-oriented programming to efficient algorithms to how to design high-quality software.

This program begins with the basic concepts of Java, one of the top programming languages, and progresses to best practices in modern software development. Learn to create efficient algorithms using sophisticated data structures for complex computational tasks. Finally, you will develop interactive and data-driven web apps using JavaScript.

All courses are 100 percent online, and you can complete them at your own pace. You will receive a professional certificate if you pay for and complete all four courses.

100% online

6-10 hours a week

Hosted on edX

Professional Certificate

What You’ll Learn

• How to design, develop, and test large software applications using Java
• Development environments, tools, and frameworks for modern Java software development
• How to select, apply, and analyze the most appropriate data representations in your code
• How to apply object-oriented programming principles in order to develop high-quality software that is easy to read, understand, and modify
• Algorithmic thinking and problem-solving techniques
• How to create dynamic, interactive web applications using JavaScript tools and frameworks

About the Program

Courses in This Program

Software development fundamentals.

Learn the fundamentals of object-oriented programming in Java, as well as best practices of modern software development.

• 6-8 hours per week
• Instructor: Arvind Bhusnurmath

Enroll Today

Data Structures and Software Design

Learn how to select, apply, and analyze the most appropriate data representations in your code and design high quality software that is easy to understand and modify.

• Instructor: Chris Murphy

Algorithm Design and Analysis

Learn about the core principles of computer science: algorithmic thinking and computational problem solving.

• Instructor:   Sampath Kannan

Programming for the Web with JavaScript

Learn how to develop dynamic, interactive, and data-driven web apps using JavaScript.

• Instructors: Chris Murphy , Swapneel Sheth

  Enroll Today

Earn a professional certificate.

Pay for and complete four courses, and build your software development skills.

Introduction to Problem Solving

• First Online: 01 January 2015

Cite this chapter

• Ian Chivers 3 &
• Jane Sleightholme 4  

6428 Accesses

The aims of this chapter are:

To examine some of the ideas and concepts involved in problem solving.

To introduce the concept of an algorithm.

To introduce two ways of approaching algorithmic problem solving.

To introduce the ideas involved with systems analysis and design , i.e., to show the need for pencil and paper study before using a computer system.

To introduce the Unified Modeling Language—UML, a general purpose modeling language used in the field of software engineering.

They constructed ladders to reach to the top of the enemy’s wall, and they did this by calculating the height of the wall from the number of layers of bricks at a point which was facing in their direction and had not been plastered. The layers were counted by a lot of people at the same time, and though some were likely to get the figure wrong the majority would get it right...Thus, guessing what the thickness of a single brick was, they calculated how long their ladder would have to be. Thucydides, The Peloponnesian War ‘When I use a word’, Humpty Dumpty said, in a rather scornful tone, ‘it means just what I choose it to mean—neither more nor less’. ‘The question is’, said Alice, ‘whether you can make words mean so many different things’. Lewis Carroll, Through the Looking Glass and What Alice Found There It is possible to invent a single machine which can be used to compute any computable sequence. Alan Turing

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Author information

Authors and affiliations.

Rhymney Consulting, London, UK

Ian Chivers

Fortranplus, London, UK

Jane Sleightholme

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Ian Chivers .

Bibliography

Aho, A.V., Hopcroft, J.E., Ullman, J.D.: The Design and Analysis of Computer Algorithms. Addison-Wesley (1982)

Theoretical coverage of the design and analysis of computer algorithms.

Beckman, F.S.: Mathematical Foundations of Programming. Addison-Wesley, (1981)

Good clear coverage of the theoretical basis of computing.

Bulloff, J.J., Holyoke, T.C., Hahn, S.W.: Foundations of Mathematics—Symposium Papers Commemorating the 60th Birthday of Kurt Gödel. Springer (1969)

The comment by John von Neumann highlights the importance of Gödel’s work, .. Kurt Gödel’s achievement in modern logic is singular and monumental—indeed it is more than a monument, it is a landmark which will remain visible far in space and time. Whether anything comparable to it has occurred in the logic of modern times may be debated. In any case, the conceivable proxima are very, very few. The subject of logic has certainly changed its nature and possibilities with Gödel’s achievement.

Dahl, O.J., Dijkstra, E.W., Hoare, C.A.R.: Structured programming. Academic Press (1972)

This is the seminal book on structured programming.

Davis, M.: Computability and Unsolvability. Dover (1982)

The book is an introduction to the theory of computability and noncomputability—the theory of recursive functions in mathematics. Not for the mathematically faint hearted!

Davis, W.S.: Systems Analysis and Design. Addison-Wesley (1983)

Good introduction to systems analysis and design, with a variety of case studies. Also looks at some of the tools available to the systems analyst.

Edmonds, D., Eidinow, J.: Wittgensteins Poker, Faber and Faber (2001)

The subtitle of the book provides a better understanding of the content - ‘The story of a 10 minute argument between two great philosophers’, which took place on Friday 25 October 1946 at the Cambridge Moral Science Club. The title of Poppers paper was ‘Are there Philosophical problems?’. Ludwig Wittgenstein and Bertrand Russell were in the audience. Well worth a read.

Here is an extract of a quote from the Times Literary Supplement. A succinctly composed, informative, wonderfully readable and often funny account of a single impassioned encounter between the great overbearing philosopher Ludwig Wittgenstein and the younger, less great but equally overbearing philosopher Karl Popper ... reads like an inspired collaboration between Iris Murdoch and Monty Python.

Fogelin, R.J.: Wittgenstein. Routledge and Kegan Paul (1980)

The book provides a gentle introduction to the work of the philosopher Wittgenstein, who examined some of the philosophical problems associated with logic and reason.

Gödel, K.: On Formally Undecidable Propositions of Principia Mathematica and Related Systems. Oliver and Boyd (1962)

An English translation of Gödel’s original paper by Meltzer, with quite a lengthy introduction by R.B. Braithwaite, then Knightbridge Professor of Moral Philosophy at Cambridge University, England, and classified under philosophy at the library at King’s, rather than mathematics.

Hofstadter, D.: The Eternal Golden Braid. Harvester Press (1979)

A very readable coverage of paradox and contradiction in art, music and logic, looking at the work of Escher, Bach and Gödel, respectively.

Hopcroft, J.E., Ullman, J.D.: Introduction to Automata Theory, Languages and Computation. Addison-Wesley (1979)

Coverage of the theoretical basis of computing.

Jacobson, I., Grady, B., James, R.: The Unified Software Development Process. Addison Wesley Longman (1998) ISBN 0-201-57169-2.

The original book on UML.

Kernighan, B.W., Plauger, P.J.: Software Tools. Addison-Wesley (1976)

Interesting essays on the program development process, originally using a nonstandard variant of Fortran. Also available using Pascal.

Knuth, D.E.: The Art of Computer Programming. Addison-Wesley,

Vol 1. Fundamental Algorithms, 1974

Vol 2. Semi-numerical Algorithms, 1978

Vol 3. Sorting and Searching, 1972

Contains interesting insights into many aspects of algorithm design. Good source of specialist algorithms, and Knuth writes with obvious and infectious enthusiasm (and erudition).

Millington, D.: Systems Analysis and Design for Computer Applications. Ellis Horwood (1981)

Short and readable introduction to systems analysis and design.

Popper, K.: The Logic of Scientific Discovery, 1934 (as Logik der Forschung, English translation 1959). Routledge. ISBN 0-415-27844-9.

Popper argues that science should adopt a methodology based on falsifiability, because no number of experiments can ever prove a theory, but a single experiment can contradict one. A classic.

Salmon, M.H.: Logic and Critical Thinking. Harcourt Brace Jovanovich (1984)

Quite a good introduction to logic and critical thinking. Coverage of arguments, deductive and inductive arguments, causal arguments, probability and inductive logic, confirmation of hypotheses.

Wirth, N.: Algorithms + Data Structures = Programs. Prentice Hall (1976)

One of the seminal texts in computer science. Essential reading.

Wirth N.: Program development by stepwise refinement. Commun. ACM, 14 (4), 221–227 (1971)

Clear and simple exposition of the ideas of stepwise refinement.

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Chivers, I., Sleightholme, J. (2015). Introduction to Problem Solving. In: Introduction to Programming with Fortran. Springer, Cham. https://doi.org/10.1007/978-3-319-17701-4_2

Download citation

DOI : https://doi.org/10.1007/978-3-319-17701-4_2

Published : 08 August 2015

Publisher Name : Springer, Cham

Print ISBN : 978-3-319-17700-7

Online ISBN : 978-3-319-17701-4

eBook Packages : Computer Science Computer Science (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Tutorial Playlist

Programming tutorial, your guide to the best backend languages for 2024, an ultimate guide that helps you to start learn coding 2024, what is backend development: the ultimate guide for beginners, all you need to know for choosing the first programming language to learn, here’s all you need to know about coding, decoding, and reasoning with examples, understanding what is xml: the best guide to xml and its concepts., an ultimate guide to learn the importance of low-code and no-code development, top frontend languages that you should know about, top 75+ frontend developer interview questions and answers, the ultimate guide to learn typescript generics, the most comprehensive guide for beginners to know ‘what is typescript’.

The Ultimate Guide on Introduction to Competitive Programming

Top 60+ TCS NQT Interview Questions and Answers for 2024

Most commonly asked logical reasoning questions in an aptitude test, everything you need to know about advanced typescript concepts, an absolute guide to build c hello world program, a one-stop solution guide to learn how to create a game in unity, what is nat significance of nat for translating ip addresses in the network model, data science vs software engineering: key differences, a real-time chat application typescript project using node.js as a server, what is raspberry pi here’s the best guide to get started, what is arduino here’s the best beginners guide to get started, arduino vs. raspberry pi: which is the better board, the perfect guide for all you need to learn about mean stack, software developer resume: a comprehensive guide, here’s everything all you need to know about the programming roadmap, an ultimate guide that helps you to develop and improve problem solving in programming, the top 10 awesome arduino projects of all time, roles of product managers, pyspark rdd: everything you need to know about pyspark rdd, wipro interview questions and answers that you should know before going for an interview, how to use typescript with nodejs: the ultimate guide, what is rust programming language why is it so popular, software terminologies, an ultimate guide that helps you to develop and improve problem solving in programming.

Lesson 27 of 34 By Hemant Deshpande

Table of Contents

Coding and Programming skills hold a significant and critical role in implementing and developing various technologies and software. They add more value to the future and development. These programming and coding skills are essential for every person to improve problem solving skills. So, we brought you this article to help you learn and know the importance of these skills in the future. 

Want a Top Software Development Job? Start Here!

Topics covered in this problem solving in programming article are:

• What is Problem Solving in Programming? 
• Problem Solving skills in Programming
• How does it impact your career ?
• Steps involved in Problem Solving
• Steps to improve Problem Solving in programming

What is Problem Solving in Programming?

Computers are used to solve various problems in day-to-day life. Problem Solving is an essential skill that helps to solve problems in programming. There are specific steps to be carried out to solve problems in computer programming, and the success depends on how correctly and precisely we define a problem. This involves designing, identifying and implementing problems using certain steps to develop a computer.

When we know what exactly problem solving in programming is, let us learn how it impacts your career growth.

How Does It Impact Your Career?

Many companies look for candidates with excellent problem solving skills. These skills help people manage the work and make candidates put more effort into the work, which results in finding solutions for complex problems in unexpected situations. These skills also help to identify quick solutions when they arise and are identified. 

People with great problem solving skills also possess more thinking and analytical skills, which makes them much more successful and confident in their career and able to work in any kind of environment. 

The above section gives you an idea of how problem solving in programming impacts your career and growth. Now, let's understand what problem solving skills mean.

Problem Solving Skills in Programming

Solving a question that is related to computers is more complicated than finding the solutions for other questions. It requires excellent knowledge and much thinking power. Problem solving in programming skills is much needed for a person and holds a major advantage. For every question, there are specific steps to be followed to get a perfect solution. By using those steps, it is possible to find a solution quickly.

The above section is covered with an explanation of problem solving in programming skills. Now let's learn some steps involved in problem solving.

Steps Involved in Problem Solving

Before being ready to solve a problem, there are some steps and procedures to be followed to find the solution. Let's have a look at them in this problem solving in programming article.

Basically, they are divided into four categories:

• Analysing the problem
• Developing the algorithm
• Testing and debugging

Analysing the Problem

Every problem has a perfect solution; before we are ready to solve a problem, we must look over the question and understand it. When we know the question, it is easy to find the solution for it. If we are not ready with what we have to solve, then we end up with the question and cannot find the answer as expected. By analysing it, we can figure out the outputs and inputs to be carried out. Thus, when we analyse and are ready with the list, it is easy and helps us find the solution easily. 

Developing the Algorithm

It is required to decide a solution before writing a program. The procedure of representing the solution  in a natural language called an algorithm. We must design, develop and decide the final approach after a number of trials and errors, before actually writing the final code on an algorithm before we write the code. It captures and refines all the aspects of the desired solution.

Once we finalise the algorithm, we must convert the decided algorithm into a code or program using a dedicated programming language that is understandable by the computer to find a desired solution. In this stage, a wide variety of programming languages are used to convert the algorithm into code. 

Testing and Debugging

The designed and developed program undergoes several rigorous tests based on various real-time parameters and the program undergoes various levels of simulations. It must meet the user's requirements, which have to respond with the required time. It should generate all expected outputs to all the possible inputs. The program should also undergo bug fixing and all possible exception handling. If it fails to show the possible results, it should be checked for logical errors.

Industries follow some testing methods like system testing, component testing and acceptance testing while developing complex applications. The errors identified while testing are debugged or rectified and tested again until all errors are removed from the program.

The steps mentioned above are involved in problem solving in programming. Now let's see some more detailed information about the steps to improve problem solving in programming.

Steps to Improve Problem Solving in Programming

Right mindset.

The way to approach problems is the key to improving the skills. To find a solution, a positive mindset helps to solve problems quickly. If you think something is impossible, then it is hard to achieve. When you feel free and focus with a positive attitude, even complex problems will have a perfect solution.

Making Right Decisions

When we need to solve a problem, we must be clear with the solution. The perfect solution helps to get success in a shorter period. Making the right decisions in the right situation helps to find the perfect solution quickly and efficiently. These skills also help to get more command over the subject.

Keeping Ideas on Track

Ideas always help much in improving the skills; they also help to gain more knowledge and more command over things. In problem solving situations, these ideas help much and help to develop more skills. Give opportunities for the mind and keep on noting the ideas.

Learning from Feedbacks

A crucial part of learning is from the feedback. Mistakes help you to gain more knowledge and have much growth. When you have a solution for a problem, go for the feedback from the experienced or the professionals. It helps you get success within a shorter period and enables you to find other solutions easily.

Asking Questions

Questions are an incredible part of life. While searching for solutions, there are a lot of questions that arise in our minds. Once you know the question correctly, then you are able to find answers quickly. In coding or programming, we must have a clear idea about the problem. Then, you can find the perfect solution for it. Raising questions can help to understand the problem.

These are a few reasons and tips to improve problem solving in programming skills. Now let's see some major benefits in this article.

• Problem solving in programming skills helps to gain more knowledge over coding and programming, which is a major benefit.
• These problem solving skills also help to develop more skills in a person and build a promising career.
• These skills also help to find the solutions for critical and complex problems in a perfect way.
• Learning and developing problem solving in programming helps in building a good foundation.
• Most of the companies are looking for people with good problem solving skills, and these play an important role when it comes to job opportunities 

Don't miss out on the opportunity to become a Certified Professional with Simplilearn's Post Graduate Program in Full Stack Web Development . Enroll Today!

Problem solving in programming skills is important in this modern world; these skills build a great career and hold a great advantage. This article on problem solving in programming provides you with an idea of how it plays a massive role in the present world. In this problem solving in programming article, the skills and the ways to improve more command on problem solving in programming are mentioned and explained in a proper way.

If you are looking to advance in your career. Simplilearn provides training and certification courses on various programming languages - Python , Java , Javascript , and many more. Check out our Post Graduate Program in Full Stack Web Development course that will help you excel in your career.

If you have any questions for us on the problem solving in programming article. Do let us know in the comments section below; we have our experts answer it right away.

About the Author

Hemant Deshpande, PMP has more than 17 years of experience working for various global MNC's. He has more than 10 years of experience in managing large transformation programs for Fortune 500 clients across verticals such as Banking, Finance, Insurance, Healthcare, Telecom and others. During his career he has worked across the geographies - North America, Europe, Middle East, and Asia Pacific. Hemant is an internationally Certified Executive Coach (CCA/ICF Approved) working with corporate leaders. He also provides Management Consulting and Training services. He is passionate about writing and regularly blogs and writes content for top websites. His motto in life - Making a positive difference.

Recommended Resources

Your One-Stop Solution to Understand Coin Change Problem

Combating the Global Talent Shortage Through Skill Development Programs

What Is Problem Solving? Steps, Techniques, and Best Practices Explained

One Stop Solution to All the Dynamic Programming Problems

The Ultimate Guide to Top Front End and Back End Programming Languages for 2021

• PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

Introduction to Problem Solving Skills

What is problem solving and why is it important.

The ability to solve problems is a basic life skill and is essential to our day-to-day lives, at home, at school, and at work. We solve problems every day without really thinking about how we solve them. For example: it’s raining and you need to go to the store. What do you do? There are lots of possible solutions. Take your umbrella and walk. If you don't want to get wet, you can drive, or take the bus. You might decide to call a friend for a ride, or you might decide to go to the store another day. There is no right way to solve this problem and different people will solve it differently.

Problem solving is the process of identifying a problem, developing possible solution paths, and taking the appropriate course of action.

Why is problem solving important? Good problem solving skills empower you not only in your personal life but are critical in your professional life. In the current fast-changing global economy, employers often identify everyday problem solving as crucial to the success of their organizations. For employees, problem solving can be used to develop practical and creative solutions, and to show independence and initiative to employers.

Throughout this case study you will be asked to jot down your thoughts in idea logs. These idea logs are used for reflection on concepts and for answering short questions. When you click on the "Next" button, your responses will be saved for that page. If you happen to close the webpage, you will lose your work on the page you were on, but previous pages will be saved. At the end of the case study, click on the "Finish and Export to PDF" button to acknowledge completion of the case study and receive a PDF document of your idea logs.

What Does Problem Solving Look Like?

The ability to solve problems is a skill, and just like any other skill, the more you practice, the better you get. So how exactly do you practice problem solving? Learning about different problem solving strategies and when to use them will give you a good start. Problem solving is a process. Most strategies provide steps that help you identify the problem and choose the best solution. There are two basic types of strategies: algorithmic and heuristic.

Algorithmic strategies are traditional step-by-step guides to solving problems. They are great for solving math problems (in algebra: multiply and divide, then add or subtract) or for helping us remember the correct order of things (a mnemonic such as “Spring Forward, Fall Back” to remember which way the clock changes for daylight saving time, or “Righty Tighty, Lefty Loosey” to remember what direction to turn bolts and screws). Algorithms are best when there is a single path to the correct solution.

But what do you do when there is no single solution for your problem? Heuristic methods are general guides used to identify possible solutions. A popular one that is easy to remember is IDEAL [ Bransford & Stein, 1993 ] :

• I dentify the problem
• D efine the context of the problem
• E xplore possible strategies
• A ct on best solution

IDEAL is just one problem solving strategy. Building a toolbox of problem solving strategies will improve your problem solving skills. With practice, you will be able to recognize and use multiple strategies to solve complex problems.

Watch the video

What is the best way to get a peanut out of a tube that cannot be moved? Watch a chimpanzee solve this problem in the video below [ Geert Stienissen, 2010 ].

[PDF transcript]

Describe the series of steps you think the chimpanzee used to solve this problem.

• [Page 2: What does Problem Solving Look Like?] Describe the series of steps you think the chimpanzee used to solve this problem.

Think of an everyday problem you've encountered recently and describe your steps for solving it.

• [Page 2: What does Problem Solving Look Like?] Think of an everyday problem you've encountered recently and describe your steps for solving it.

Developing Problem Solving Processes

Problem solving is a process that uses steps to solve problems. But what does that really mean? Let's break it down and start building our toolbox of problem solving strategies.

What is the first step of solving any problem? The first step is to recognize that there is a problem and identify the right cause of the problem. This may sound obvious, but similar problems can arise from different events, and the real issue may not always be apparent. To really solve the problem, it's important to find out what started it all. This is called identifying the root cause .

Example: You and your classmates have been working long hours on a project in the school's workshop. The next afternoon, you try to use your student ID card to access the workshop, but discover that your magnetic strip has been demagnetized. Since the card was a couple of years old, you chalk it up to wear and tear and get a new ID card. Later that same week you learn that several of your classmates had the same problem! After a little investigation, you discover that a strong magnet was stored underneath a workbench in the workshop. The magnet was the root cause of the demagnetized student ID cards.

The best way to identify the root cause of the problem is to ask questions and gather information. If you have a vague problem, investigating facts is more productive than guessing a solution. Ask yourself questions about the problem. What do you know about the problem? What do you not know? When was the last time it worked correctly? What has changed since then? Can you diagram the process into separate steps? Where in the process is the problem occurring? Be curious, ask questions, gather facts, and make logical deductions rather than assumptions.

Watch Adam Savage from Mythbusters, describe his problem solving process [ ForaTv, 2010 ]. As you watch this section of the video, try to identify the questions he asks and the different strategies he uses.

Adam Savage shared many of his problem solving processes. List the ones you think are the five most important. Your list may be different from other people in your class—that's ok!

• [Page 3: Developing Problem Solving Processes] Adam Savage shared many of his problem solving processes. List the ones you think are the five most important.

“The ability to ask the right question is more than half the battle of finding the answer.” — Thomas J. Watson , founder of IBM

Voices From the Field: Solving Problems

In manufacturing facilities and machine shops, everyone on the floor is expected to know how to identify problems and find solutions. Today's employers look for the following skills in new employees: to analyze a problem logically, formulate a solution, and effectively communicate with others.

In this video, industry professionals share their own problem solving processes, the problem solving expectations of their employees, and an example of how a problem was solved.

Meet the Partners:

• Taconic High School in Pittsfield, Massachusetts, is a comprehensive, fully accredited high school with special programs in Health Technology, Manufacturing Technology, and Work-Based Learning.
• Berkshire Community College in Pittsfield, Massachusetts, prepares its students with applied manufacturing technical skills, providing hands-on experience at industrial laboratories and manufacturing facilities, and instructing them in current technologies.
• H.C. Starck in Newton, Massachusetts, specializes in processing and manufacturing technology metals, such as tungsten, niobium, and tantalum. In almost 100 years of experience, they hold over 900 patents, and continue to innovate and develop new products.
• Nypro Healthcare in Devens, Massachusetts, specializes in precision injection-molded healthcare products. They are committed to good manufacturing processes including lean manufacturing and process validation.

Making Decisions

Now that you have a couple problem solving strategies in your toolbox, let's practice. In this exercise, you are given a scenario and you will be asked to decide what steps you would take to identify and solve the problem.

Scenario: You are a new employee and have just finished your training. As your first project, you have been assigned the milling of several additional components for a regular customer. Together, you and your trainer, Bill, set up for the first run. Checking your paperwork, you gather the tools and materials on the list. As you are mounting the materials on the table, you notice that you didn't grab everything and hurriedly grab a few more items from one of the bins. Once the material is secured on the CNC table, you load tools into the tool carousel in the order listed on the tool list and set the fixture offsets.

Bill tells you that since this is a rerun of a job several weeks ago, the CAD/CAM model has already been converted to CNC G-code. Bill helps you download the code to the CNC machine. He gives you the go-ahead and leaves to check on another employee. You decide to start your first run.

What problems did you observe in the video?

• [Page 5: Making Decisions] What problems did you observe in the video?
• What do you do next?
• Try to fix it yourself.
• Ask your trainer for help.

As you are cleaning up, you think about what happened and wonder why it happened. You try to create a mental picture of what happened. You are not exactly sure what the end mill hit, but it looked like it might have hit the dowel pin. You wonder if you grabbed the correct dowel pins from the bins earlier.

You can think of two possible next steps. You can recheck the dowel pin length to make sure it is the correct length, or do a dry run using the CNC single step or single block function with the spindle empty to determine what actually happened.

• Check the dowel pins.
• Use the single step/single block function to determine what happened.

You notice that your trainer, Bill, is still on the floor and decide to ask him for help. You describe the problem to him. Bill asks if you know what the end mill ran into. You explain that you are not sure but you think it was the dowel pin. Bill reminds you that it is important to understand what happened so you can fix the correct problem. He suggests that you start all over again and begin with a dry run using the single step/single block function, with the spindle empty, to determine what it hit. Or, since it happened at the end, he mentions that you can also check the G-code to make sure the Z-axis is raised before returning to the home position.

• Run the single step/single block function.
• Edit the G-code to raise the Z-axis.

You finish cleaning up and check the CNC for any damage. Luckily, everything looks good. You check your paperwork and gather the components and materials again. You look at the dowel pins you used earlier, and discover that they are not the right length. As you go to grab the correct dowel pins, you have to search though several bins. For the first time, you are aware of the mess - it looks like the dowel pins and other items have not been put into the correctly labeled bins. You spend 30 minutes straightening up the bins and looking for the correct dowel pins.

Finally finding them, you finish setting up. You load tools into the tool carousel in the order listed on the tool list and set the fixture offsets. Just to make sure, you use the CNC single step/single block function, to do a dry run of the part. Everything looks good! You are ready to create your first part. The first component is done, and, as you admire your success, you notice that the part feels hotter than it should.

You wonder why? You go over the steps of the process to mentally figure out what could be causing the residual heat. You wonder if there is a problem with the CNC's coolant system or if the problem is in the G-code.

• Look at the G-code.

After thinking about the problem, you decide that maybe there's something wrong with the setup. First, you clean up the damaged materials and remove the broken tool. You check the CNC machine carefully for any damage. Luckily, everything looks good. It is time to start over again from the beginning.

You again check your paperwork and gather the tools and materials on the setup sheet. After securing the new materials, you use the CNC single step/single block function with the spindle empty, to do a dry run of the part. You watch carefully to see if you can figure out what happened. It looks to you like the spindle barely misses hitting the dowel pin. You determine that the end mill was broken when it hit the dowel pin while returning to the start position.

After conducting a dry run using the single step/single block function, you determine that the end mill was damaged when it hit the dowel pin on its return to the home position. You discuss your options with Bill. Together, you decide the best thing to do would be to edit the G-code and raise the Z-axis before returning to home. You open the CNC control program and edit the G-code. Just to make sure, you use the CNC single step/single block function, to do another dry run of the part. You are ready to create your first part. It works. You first part is completed. Only four more to go.

As you are cleaning up, you notice that the components are hotter than you expect and the end mill looks more worn than it should be. It dawns on you that while you were milling the component, the coolant didn't turn on. You wonder if it is a software problem in the G-code or hardware problem with the CNC machine.

It's the end of the day and you decide to finish the rest of the components in the morning.

• You decide to look at the G-code in the morning.
• You leave a note on the machine, just in case.

You decide that the best thing to do would be to edit the G-code and raise the Z-axis of the spindle before it returns to home. You open the CNC control program and edit the G-code.

While editing the G-code to raise the Z-axis, you notice that the coolant is turned off at the beginning of the code and at the end of the code. The coolant command error caught your attention because your coworker, Mark, mentioned having a similar issue during lunch. You change the coolant command to turn the mist on.

• You decide to talk with your supervisor.
• You discuss what happened with a coworker over lunch.

As you reflect on the residual heat problem, you think about the machining process and the factors that could have caused the issue. You try to think of anything and everything that could be causing the issue. Are you using the correct tool for the specified material? Are you using the specified material? Is it running at the correct speed? Is there enough coolant? Are there chips getting in the way?

Wait, was the coolant turned on? As you replay what happened in your mind, you wonder why the coolant wasn't turned on. You decide to look at the G-code to find out what is going on.

From the milling machine computer, you open the CNC G-code. You notice that there are no coolant commands. You add them in and on the next run, the coolant mist turns on and the residual heat issues is gone. Now, its on to creating the rest of the parts.

Have you ever used brainstorming to solve a problem? Chances are, you've probably have, even if you didn't realize it.

You notice that your trainer, Bill, is on the floor and decide to ask him for help. You describe the problem with the end mill breaking, and how you discovered that items are not being returned to the correctly labeled bins. You think this caused you to grab the incorrect length dowel pins on your first run. You have sorted the bins and hope that the mess problem is fixed. You then go on to tell Bill about the residual heat issue with the completed part.

Together, you go to the milling machine. Bill shows you how to check the oil and coolant levels. Everything looks good at the machine level. Next, on the CNC computer, you open the CNC G-code. While looking at the code, Bill points out that there are no coolant commands. Bill adds them in and when you rerun the program, it works.

Bill is glad you mentioned the problem to him. You are the third worker to mention G-code issues over the last week. You noticed the coolant problems in your G-code, John noticed a Z-axis issue in his G-code, and Sam had issues with both the Z-axis and the coolant. Chances are, there is a bigger problem and Bill will need to investigate the root cause .

Talking with Bill, you discuss the best way to fix the problem. Bill suggests editing the G-code to raise the Z-axis of the spindle before it returns to its home position. You open the CNC control program and edit the G-code. Following the setup sheet, you re-setup the job and use the CNC single step/single block function, to do another dry run of the part. Everything looks good, so you run the job again and create the first part. It works. Since you need four of each component, you move on to creating the rest of them before cleaning up and leaving for the day.

It's a new day and you have new components to create. As you are setting up, you go in search of some short dowel pins. You discover that the bins are a mess and components have not been put away in the correctly labeled bins. You wonder if this was the cause of yesterday's problem. As you reorganize the bins and straighten up the mess, you decide to mention the mess issue to Bill in your afternoon meeting.

You describe the bin mess and using the incorrect length dowels to Bill. He is glad you mentioned the problem to him. You are not the first person to mention similar issues with tools and parts not being put away correctly. Chances are there is a bigger safety issue here that needs to be addressed in the next staff meeting.

In any workplace, following proper safety and cleanup procedures is always important. This is especially crucial in manufacturing where people are constantly working with heavy, costly and sometimes dangerous equipment. When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Effective communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost, and save money.

You now know that the end mill was damaged when it hit the dowel pin. It seems to you that the easiest thing to do would be to edit the G-code and raise the Z-axis position of the spindle before it returns to the home position. You open the CNC control program and edit the G-code, raising the Z-axis. Starting over, you follow the setup sheet and re-setup the job. This time, you use the CNC single step/single block function, to do another dry run of the part. Everything looks good, so you run the job again and create the first part.

At the end of the day, you are reviewing your progress with your trainer, Bill. After you describe the day's events, he reminds you to always think about safety and the importance of following work procedures. He decides to bring the issue up in the next morning meeting as a reminder to everyone.

In any workplace, following proper procedures (especially those that involve safety) is always important. This is especially crucial in manufacturing where people are constantly working with heavy, costly, and sometimes dangerous equipment. When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Effective communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost, and save money. One tool to improve communication is the morning meeting or huddle.

The next morning, you check the G-code to determine what is wrong with the coolant. You notice that the coolant is turned off at the beginning of the code and also at the end of the code. This is strange. You change the G-code to turn the coolant on at the beginning of the run and off at the end. This works and you create the rest of the parts.

Throughout the day, you keep wondering what caused the G-code error. At lunch, you mention the G-code error to your coworker, John. John is not surprised. He said that he encountered a similar problem earlier this week. You decide to talk with your supervisor the next time you see him.

You are in luck. You see your supervisor by the door getting ready to leave. You hurry over to talk with him. You start off by telling him about how you asked Bill for help. Then you tell him there was a problem and the end mill was damaged. You describe the coolant problem in the G-code. Oh, and by the way, John has seen a similar problem before.

Your supervisor doesn't seem overly concerned, errors happen. He tells you "Good job, I am glad you were able to fix the issue." You are not sure whether your supervisor understood your explanation of what happened or that it had happened before.

The challenge of communicating in the workplace is learning how to share your ideas and concerns. If you need to tell your supervisor that something is not going well, it is important to remember that timing, preparation, and attitude are extremely important.

It is the end of your shift, but you want to let the next shift know that the coolant didn't turn on. You do not see your trainer or supervisor around. You decide to leave a note for the next shift so they are aware of the possible coolant problem. You write a sticky note and leave it on the monitor of the CNC control system.

How effective do you think this solution was? Did it address the problem?

In this scenario, you discovered several problems with the G-code that need to be addressed. When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Effective communication is an important tool because it can prevent problems from recurring and avoid injury to personnel. The challenge of communicating in the workplace is learning how and when to share your ideas and concerns. If you need to tell your co-workers or supervisor that there is a problem, it is important to remember that timing and the method of communication are extremely important.

You are able to fix the coolant problem in the G-code. While you are glad that the problem is fixed, you are worried about why it happened in the first place. It is important to remember that if a problem keeps reappearing, you may not be fixing the right problem. You may only be addressing the symptoms.

You decide to talk to your trainer. Bill is glad you mentioned the problem to him. You are the third worker to mention G-code issues over the last week. You noticed the coolant problems in your G-code, John noticed a Z-axis issue in his G-code, and Sam had issues with both the Z-axis and the coolant. Chances are, there is a bigger problem and Bill will need to investigate the root cause .

Over lunch, you ask your coworkers about the G-code problem and what may be causing the error. Several people mention having similar problems but do not know the cause.

You have now talked to three coworkers who have all experienced similar coolant G-code problems. You make a list of who had the problem, when they had the problem, and what each person told you.

When you see your supervisor later that afternoon, you are ready to talk with him. You describe the problem you had with your component and the damaged bit. You then go on to tell him about talking with Bill and discovering the G-code issue. You show him your notes on your coworkers' coolant issues, and explain that you think there might be a bigger problem.

You supervisor thanks you for your initiative in identifying this problem. It sounds like there is a bigger problem and he will need to investigate the root cause. He decides to call a team huddle to discuss the issue, gather more information, and talk with the team about the importance of communication.

Root Cause Analysis

Root cause analysis ( RCA ) is a method of problem solving that identifies the underlying causes of an issue. Root cause analysis helps people answer the question of why the problem occurred in the first place. RCA uses clear cut steps in its associated tools, like the "5 Whys Analysis" and the "Cause and Effect Diagram," to identify the origin of the problem, so that you can:

• Determine what happened.
• Determine why it happened.
• Fix the problem so it won’t happen again.

RCA works under the idea that systems and events are connected. An action in one area triggers an action in another, and another, and so on. By tracing back these actions, you can discover where the problem started and how it developed into the problem you're now facing. Root cause analysis can prevent problems from recurring, reduce injury to personnel, reduce rework and scrap, and ultimately, reduce cost and save money. There are many different RCA techniques available to determine the root cause of a problem. These are just a few:

• Root Cause Analysis Tools
• 5 Whys Analysis
• Fishbone or Cause and Effect Diagram
• Pareto Analysis

How Huddles Work

Communication is a vital part of any setting where people work together. Effective communication helps employees and managers form efficient teams. It builds trusts between employees and management, and reduces unnecessary competition because each employee knows how their part fits in the larger goal.

One tool that management can use to promote communication in the workplace is the huddle . Just like football players on the field, a huddle is a short meeting where everyone is standing in a circle. A daily team huddle ensures that team members are aware of changes to the schedule, reiterated problems and safety issues, and how their work impacts one another. When done right, huddles create collaboration, communication, and accountability to results. Impromptu huddles can be used to gather information on a specific issue and get each team member's input.

The most important thing to remember about huddles is that they are short, lasting no more than 10 minutes, and their purpose is to communicate and identify. In essence, a huddle’s purpose is to identify priorities, communicate essential information, and discover roadblocks to productivity.

Who uses huddles? Many industries and companies use daily huddles. At first thought, most people probably think of hospitals and their daily patient update meetings, but lots of managers use daily meetings to engage their employees. Here are a few examples:

• Brian Scudamore, CEO of 1-800-Got-Junk? , uses the daily huddle as an operational tool to take the pulse of his employees and as a motivational tool. Watch a morning huddle meeting .
• Fusion OEM, an outsourced manufacturing and production company. What do employees take away from the daily huddle meeting .
• Biz-Group, a performance consulting group. Tips for a successful huddle .

Brainstorming

One tool that can be useful in problem solving is brainstorming . Brainstorming is a creativity technique designed to generate a large number of ideas for the solution to a problem. The method was first popularized in 1953 by Alex Faickney Osborn in the book Applied Imagination . The goal is to come up with as many ideas as you can in a fixed amount of time. Although brainstorming is best done in a group, it can be done individually. Like most problem solving techniques, brainstorming is a process.

• Define a clear objective.
• Have an agreed a time limit.
• During the brainstorming session, write down everything that comes to mind, even if the idea sounds crazy.
• If one idea leads to another, write down that idea too.
• Combine and refine ideas into categories of solutions.
• Assess and analyze each idea as a potential solution.

When used during problem solving, brainstorming can offer companies new ways of encouraging staff to think creatively and improve production. Brainstorming relies on team members' diverse experiences, adding to the richness of ideas explored. This means that you often find better solutions to the problems. Team members often welcome the opportunity to contribute ideas and can provide buy-in for the solution chosen—after all, they are more likely to be committed to an approach if they were involved in its development. What's more, because brainstorming is fun, it helps team members bond.

• Watch Peggy Morgan Collins, a marketing executive at Power Curve Communications discuss How to Stimulate Effective Brainstorming .
• Watch Kim Obbink, CEO of Filter Digital, a digital content company, and her team share their top five rules for How to Effectively Generate Ideas .

Importance of Good Communication and Problem Description

Communication is one of the most frequent activities we engage in on a day-to-day basis. At some point, we have all felt that we did not effectively communicate an idea as we would have liked. The key to effective communication is preparation. Rather than attempting to haphazardly improvise something, take a few minutes and think about what you want say and how you will say it. If necessary, write yourself a note with the key points or ideas in the order you want to discuss them. The notes can act as a reminder or guide when you talk to your supervisor.

Tips for clear communication of an issue:

• Provide a clear summary of your problem. Start at the beginning, give relevant facts, timelines, and examples.
• Avoid including your opinion or personal attacks in your explanation.
• Avoid using words like "always" or "never," which can give the impression that you are exaggerating the problem.
• If this is an ongoing problem and you have collected documentation, give it to your supervisor once you have finished describing the problem.
• Remember to listen to what's said in return; communication is a two-way process.

Not all communication is spoken. Body language is nonverbal communication that includes your posture, your hands and whether you make eye contact. These gestures can be subtle or overt, but most importantly they communicate meaning beyond what is said. When having a conversation, pay attention to how you stand. A stiff position with arms crossed over your chest may imply that you are being defensive even if your words state otherwise. Shoving your hands in your pockets when speaking could imply that you have something to hide. Be wary of using too many hand gestures because this could distract listeners from your message.

The challenge of communicating in the workplace is learning how and when to share your ideas or concerns. If you need to tell your supervisor or co-worker about something that is not going well, keep in mind that good timing and good attitude will go a long way toward helping your case.

Like all skills, effective communication needs to be practiced. Toastmasters International is perhaps the best known public speaking organization in the world. Toastmasters is open to anyone who wish to improve their speaking skills and is willing to put in the time and effort to do so. To learn more, visit Toastmasters International .

Methods of Communication

Communication of problems and issues in any workplace is important, particularly when safety is involved. It is therefore crucial in manufacturing where people are constantly working with heavy, costly, and sometimes dangerous equipment. As issues and problems arise, they need to be addressed in an efficient and timely manner. Effective communication is an important skill because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost and save money.

There are many different ways to communicate: in person, by phone, via email, or written. There is no single method that fits all communication needs, each one has its time and place.

In person: In the workplace, face-to-face meetings should be utilized whenever possible. Being able to see the person you need to speak to face-to-face gives you instant feedback and helps you gauge their response through their body language. Be careful of getting sidetracked in conversation when you need to communicate a problem.

Email: Email has become the communication standard for most businesses. It can be accessed from almost anywhere and is great for things that don’t require an immediate response. Email is a great way to communicate non-urgent items to large amounts of people or just your team members. One thing to remember is that most people's inboxes are flooded with emails every day and unless they are hyper vigilant about checking everything, important items could be missed. For issues that are urgent, especially those around safety, email is not always be the best solution.

Phone: Phone calls are more personal and direct than email. They allow us to communicate in real time with another person, no matter where they are. Not only can talking prevent miscommunication, it promotes a two-way dialogue. You don’t have to worry about your words being altered or the message arriving on time. However, mobile phone use and the workplace don't always mix. In particular, using mobile phones in a manufacturing setting can lead to a variety of problems, cause distractions, and lead to serious injury.

Written: Written communication is appropriate when detailed instructions are required, when something needs to be documented, or when the person is too far away to easily speak with over the phone or in person.

There is no "right" way to communicate, but you should be aware of how and when to use the appropriate form of communication for your situation. When deciding the best way to communicate with a co-worker or manager, put yourself in their shoes, and think about how you would want to learn about the issue. Also, consider what information you would need to know to better understand the issue. Use your good judgment of the situation and be considerate of your listener's viewpoint.

Did you notice any other potential problems in the previous exercise?

• [Page 6:] Did you notice any other potential problems in the previous exercise?

Summary of Strategies

In this exercise, you were given a scenario in which there was a problem with a component you were creating on a CNC machine. You were then asked how you wanted to proceed. Depending on your path through this exercise, you might have found an easy solution and fixed it yourself, asked for help and worked with your trainer, or discovered an ongoing G-code problem that was bigger than you initially thought.

When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost, and save money. Although, each path in this exercise ended with a description of a problem solving tool for your toolbox, the first step is always to identify the problem and define the context in which it happened.

There are several strategies that can be used to identify the root cause of a problem. Root cause analysis (RCA) is a method of problem solving that helps people answer the question of why the problem occurred. RCA uses a specific set of steps, with associated tools like the “5 Why Analysis" or the “Cause and Effect Diagram,” to identify the origin of the problem, so that you can:

Once the underlying cause is identified and the scope of the issue defined, the next step is to explore possible strategies to fix the problem.

If you are not sure how to fix the problem, it is okay to ask for help. Problem solving is a process and a skill that is learned with practice. It is important to remember that everyone makes mistakes and that no one knows everything. Life is about learning. It is okay to ask for help when you don’t have the answer. When you collaborate to solve problems you improve workplace communication and accelerates finding solutions as similar problems arise.

One tool that can be useful for generating possible solutions is brainstorming . Brainstorming is a technique designed to generate a large number of ideas for the solution to a problem. The method was first popularized in 1953 by Alex Faickney Osborn in the book Applied Imagination. The goal is to come up with as many ideas as you can, in a fixed amount of time. Although brainstorming is best done in a group, it can be done individually.

Depending on your path through the exercise, you may have discovered that a couple of your coworkers had experienced similar problems. This should have been an indicator that there was a larger problem that needed to be addressed.

In any workplace, communication of problems and issues (especially those that involve safety) is always important. This is especially crucial in manufacturing where people are constantly working with heavy, costly, and sometimes dangerous equipment. When issues and problems arise, it is important that they be addressed in an efficient and timely manner. Effective communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost and save money.

One strategy for improving communication is the huddle . Just like football players on the field, a huddle is a short meeting with everyone standing in a circle. A daily team huddle is a great way to ensure that team members are aware of changes to the schedule, any problems or safety issues are identified and that team members are aware of how their work impacts one another. When done right, huddles create collaboration, communication, and accountability to results. Impromptu huddles can be used to gather information on a specific issue and get each team member's input.

To learn more about different problem solving strategies, choose an option below. These strategies accompany the outcomes of different decision paths in the problem solving exercise.

• View Problem Solving Strategies Select a strategy below... Root Cause Analysis How Huddles Work Brainstorming Importance of Good Problem Description Methods of Communication

Communication is one of the most frequent activities we engage in on a day-to-day basis. At some point, we have all felt that we did not effectively communicate an idea as we would have liked. The key to effective communication is preparation. Rather than attempting to haphazardly improvise something, take a few minutes and think about what you want say and how you will say it. If necessary, write yourself a note with the key points or ideas in the order you want to discuss them. The notes can act as a reminder or guide during your meeting.

• Provide a clear summary of the problem. Start at the beginning, give relevant facts, timelines, and examples.

In person: In the workplace, face-to-face meetings should be utilized whenever possible. Being able to see the person you need to speak to face-to-face gives you instant feedback and helps you gauge their response in their body language. Be careful of getting sidetracked in conversation when you need to communicate a problem.

There is no "right" way to communicate, but you should be aware of how and when to use the appropriate form of communication for the situation. When deciding the best way to communicate with a co-worker or manager, put yourself in their shoes, and think about how you would want to learn about the issue. Also, consider what information you would need to know to better understand the issue. Use your good judgment of the situation and be considerate of your listener's viewpoint.

"Never try to solve all the problems at once — make them line up for you one-by-one.” — Richard Sloma

Problem Solving: An Important Job Skill

Problem solving improves efficiency and communication on the shop floor. It increases a company's efficiency and profitability, so it's one of the top skills employers look for when hiring new employees. Recent industry surveys show that employers consider soft skills, such as problem solving, as critical to their business’s success.

The 2011 survey, "Boiling Point? The skills gap in U.S. manufacturing ," polled over a thousand manufacturing executives who reported that the number one skill deficiency among their current employees is problem solving, which makes it difficult for their companies to adapt to the changing needs of the industry.

In this video, industry professionals discuss their expectations and present tips for new employees joining the manufacturing workforce.

Quick Summary

• [Quick Summary: Question1] What are two things you learned in this case study?
• What question(s) do you still have about the case study?
• [Quick Summary: Question2] What question(s) do you still have about the case study?
• Is there anything you would like to learn more about with respect to this case study?
• [Quick Summary: Question3] Is there anything you would like to learn more about with respect to this case study?

• Trending Now
• Foundational Courses
• Data Science
• Practice Problem
• Machine Learning
• System Design
• DevOps Tutorial

Programming Tutorial | Introduction, Basic Concepts, Getting started, Problems

• Basic Programming Problems
• Basics of Computer Programming For Beginners
• What is Programming? A Handbook for Beginners
• Top 10 Programming Tips For Beginners
• C++ Tutorial | Learn C++ Programming
• How To Make Competitive Programming Interesting?
• CBSE Class 11 | Concepts of Programming Methodology
• 10 Programming Books That Every Programmer Must Read Once
• Hello World Program : First program while learning Programming
• Best Courses on Competitive Programming
• Career Path After Learning Basic Programming Languages & Data Structures
• 6 Steps to Learn and Master a Programming Language
• A competitive programmer's interview
• C Exercises - Practice Questions with Solutions for C Programming
• C Program to Create and Print Database of Students Using Structure
• Tips for Designing a Plan to Learn Programming & Development
• R Programming Exercises, Practice Questions and Solutions
• 10 All-Time Favorite Programming Books - Must Read in 2021!
• Competitive Programming - A Complete Guide
• Program to print ASCII Value of a character
• Introduction of Programming Paradigms
• Program for Hexadecimal to Decimal
• Program for Decimal to Octal Conversion
• A Freshers Guide To Programming
• ASCII Vs UNICODE
• How to learn Pattern printing easily?
• Loop Unrolling
• How to Learn Programming?
• Program to Print the Trapezium Pattern

This comprehensive guide of Programming Tutorial or Coding Tutorial provides an introduction to programming, covering basic concepts, setting up your development environment, and common beginner problems. Learn about variables, data types, control flow statements, functions, and how to write your first code in various languages. Explore resources and tips to help you to begin your programming journey. We designed this Programming Tutorial or Coding Tutorial to empower beginners and equip them with the knowledge and resources they will need to get started with programming.

Programming Tutorial

1. What is Programming?

Programming , also known as coding , is the process of creating a set of instructions that tell a computer how to perform a specific task. These instructions, called programs, are written in a language that the computer can understand and execute.

Table of Content

• What is Programming?
• Getting Started with Programming
• Common Programming Mistakes and How to Avoid Them
• Basic Programming EssentialsA Beginner’s Guide to Programming Fundamentals
• Advanced Programming Concepts
• Writing Your First Code
• Top 20 Programs to get started with Coding/Programming
• Next Steps after learning basic Coding/Programming
• Resources and Further Learning
• Frequently Asked Questions (FAQs) on Programming Tutorial

Think of programming as giving commands to a robot. You tell the robot what to do, step-by-step, and it follows your instructions precisely. Similarly, you tell the computer what to do through code, and it performs those tasks as instructed.

The purpose of programming is to solve problems and automate tasks. By creating programs, we can instruct computers to perform a wide range of activities, from simple calculations to complex tasks like managing databases and designing video games.

A. How Programming Works:

Programming involves several key steps:

• Problem definition: Clearly define the problem you want to solve and what you want the program to achieve.
• Algorithm design: Develop a step-by-step procedure for solving the problem.
• Coding: Translate the algorithm into a programming language using a text editor or integrated development environment (IDE).
• Testing and debugging: Run the program and identify and fix any errors.
• Deployment: Share the program with others or use it for your own purposes.

B. Benefits of Learning to Code:

Learning to code offers numerous benefits, both personal and professional:

• Develop critical thinking and problem-solving skills: Programming encourages logical thinking, problem decomposition, and finding creative solutions.
• Boost your creativity and innovation: Coding empowers you to build your own tools and applications, turning ideas into reality.
• Increase your employability: The demand for skilled programmers is high and growing across various industries.
• Improve your communication and collaboration skills: Working with code often requires collaboration and clear communication.
• Gain a deeper understanding of technology: Learning to code gives you a better understanding of how computers work and how they are used in the world around you.
• Build self-confidence and motivation: Successfully completing programming projects can boost your confidence and motivate you to learn new things.

Whether you’re interested in pursuing a career in technology or simply want to expand your knowledge and skills, learning to code is a valuable investment in your future.

2. Getting Started with Programming Tutorial

A. choosing your first language.

Assess Resource Availability:

• Free Online Resources: Platforms like Geeksforgeeks, Coursera, edX, and Udemy offer structured learning paths for various languages.
• Paid Online Courses: Platforms like Geeksforgeeks, Coursera, edX, and Udemy offer structured learning paths for various languages.
• Books and eBooks: Numerous beginner-friendly books and ebooks are available for most popular languages.
• Community Support: Look for active online forums, communities, and Stack Overflow for troubleshooting and questions.

B. Which Programming Language should you choose as your First Language?

Here’s a breakdown of popular beginner-friendly languages with their Strengths and Weaknesses:

C. Setting Up Your Development Environment

Choose a Text Editor or IDE :

• Text Editors: Sublime Text, Atom, Notepad++ (lightweight, good for beginners)
• Offline IDEs: Visual Studio Code, PyCharm, IntelliJ IDEA (feature-rich, recommended for larger projects)
• Online IDEs: GeeksforGeeks IDE

Install a Compiler or Interpreter:

• Compilers: Convert code to machine language (C++, Java)
• I nterpreters: Execute code line by line (Python, JavaScript)

Download Additional Software (if needed):

• Web browsers (Chromium, Firefox) for web development
• Android Studio or Xcode for mobile app development
• Game engines (Unity, Unreal Engine) for game development

Test Your Environment:

• Write a simple program (e.g., print “Hello, world!”)
• Run the program and verify the output
• Ensure everything is set up correctly
• Start with a simple editor like Sublime Text for code basics.
• Use an IDE like Visual Studio Code for larger projects with advanced features.
• Join online communities or forums for help with setup issues.

3. Common Programming Mistakes and How to Avoid Them

• Syntax errors: Typographical errors or incorrect grammar in your code.
• Use syntax highlighting in your editor or IDE.
• Logical errors: Errors in the logic of your program, causing it to produce the wrong results.
• Carefully review your code and logic.
• Test your program thoroughly with different inputs.
• Use debugging tools to identify and fix issues.
• Runtime errors: Errors that occur during program execution due to unforeseen circumstances.
• Seek help from online communities or forums for specific errors.
• Start with simple programs and gradually increase complexity.
• Write clean and well-formatted code for better readability.
• Use comments to explain your code and logic.
• Practice regularly and don’t be afraid to experiment.
• Seek help from online communities or mentors when stuck.

4. Basic Programming Essentials – A Beginner’s Guide to Programming Fundamentals:

This section delves deeper into fundamental programming concepts that form the building blocks of any program.

A. Variables and Data Types:

Understanding Variable Declaration and Usage:

• Variables are containers that hold data and can be assigned different values during program execution.
• To declare a variable, you specify its name and data type, followed by an optional assignment statement.
• Example: age = 25 (declares a variable named age of type integer and assigns it the value 25).
• Variables can be reassigned new values throughout the program.

Exploring Different Data Types:

• Integers: Whole numbers without decimal points (e.g., 1, 2, -3).
• Floats: Decimal numbers with a fractional part (e.g., 3.14, 10.5).
• Booleans: True or False values used for conditions.
• Characters: Single letters or symbols (‘a’, ‘$’, ‘#’).
• Strings: Sequences of characters (“Hello, world!”).
• Other data types: Arrays, lists, dictionaries, etc. (depending on the language).

Operations with Different Data Types:

Each data type has supported operations.

• Arithmetic operators (+, -, *, /) work with integers and floats.
• Comparison operators (==, !=, >, <, >=, <=) compare values.
• Logical operators (&&, ||, !) combine conditions.
• Concatenation (+) joins strings.
• Operations with incompatible data types may lead to errors.

B. Operators and Expressions:

Arithmetic Operators:

• Perform basic mathematical calculations (+, -, *, /, %, **, //).
• % (modulo) returns the remainder after division.
• ** (power) raises a number to a certain power.
• // (floor division) discards the fractional part of the result.

Comparison Operators:

• Evaluate conditions and return True or False.
• == (equal), != (not equal), > (greater than), < (less than), >= (greater than or equal), <= (less than or equal).

Logical Operators: Combine conditions and produce True or False.

• && (and): both conditions must be True.
• || (or): at least one condition must be True.
• ! (not): reverses the truth value of a condition.

Building Expressions:

• Combine variables, operators, and constants to form expressions.
• Expressions evaluate to a single value. Example: result = age + 10 * 2 (calculates the sum of age and 20).

C. Control Flow Statements:

Conditional Statements: Control the flow of execution based on conditions.

• if-else: Executes one block of code if the condition is True and another if it’s False.
• switch-case: Executes different code blocks depending on the value of a variable.

Looping Statements: Repeat a block of code multiple times.

• for: Executes a block a specific number of times.
• while: Executes a block while a condition is True.
• do-while: Executes a block at least once and then repeats while a condition is True.

Nested Loops and Conditional Statements:

• Can be combined to create complex control flow structures.
• Inner loops run inside outer loops, allowing for nested logic.

D. Functions:

Defining and Calling Functions:

• Blocks of code that perform a specific task.
• Defined with a function name, parameters (optional), and a code block.
• Called throughout the program to execute the defined functionality.

Passing Arguments to Functions:

• Values passed to functions for processing.

Returning Values from Functions:

• Functions can return a value after execution.
• Useful for collecting results.
• A function calling itself with a modified input.
• Useful for solving problems that involve repetitive tasks with smaller inputs.

These topics provide a solid foundation for understanding programming fundamentals. Remember to practice writing code and experiment with different concepts to solidify your learning.

5. Advanced Programming Concepts

This section explores more advanced programming concepts that build upon the foundational knowledge covered earlier.

A. Object-Oriented Programming (OOP)

OOP is a programming paradigm that emphasizes the use of objects to represent real-world entities and their relationships.

1. Classes and Objects:

• Classes: Define the blueprint for objects, specifying their properties (attributes) and behaviors (methods).
• Objects: Instances of a class, with their own set of properties and methods.

2. Inheritance and Polymorphism:

• Inheritance: Allows creating new classes that inherit properties and methods from existing classes (superclasses).
• Polymorphism: Enables objects to respond differently to the same message depending on their type.

3. Encapsulation and Abstraction:

• Encapsulation: Encloses an object’s internal state and methods, hiding implementation details and exposing only a public interface.
• Abstraction: Focuses on the essential features and functionalities of an object, ignoring unnecessary details.

B. Concurrency and Parallelism

Concurrency and parallelism are crucial for improving program efficiency and responsiveness.

1. Multithreading and Multiprocessing:

• Multithreading: Allows multiple threads of execution within a single process, enabling concurrent tasks.
• Multiprocessing: Utilizes multiple processors to run different processes simultaneously, achieving true parallelism.

2. Synchronization and Concurrency Control:

Mechanisms to ensure data consistency and prevent conflicts when multiple threads or processes access shared resources.

6. Writing Your First Code

Here is your first code in different languages. These programs all achieve the same goal: printing “ Hello, world! ” to the console. However, they use different syntax and conventions specific to each language.

Printing “Hello world” in C++:

Explanation of above C++ code:

• #include: This keyword includes the <iostream> library, which provides functions for input and output.
• int main(): This defines the main function, which is the entry point of the program.
• std::cout <<: This keyword prints the following expression to the console.
• “Hello, world!” This is the string that is printed to the console.
• std::endl: This keyword inserts a newline character after the printed string.
• return 0; This statement exits the program and returns a success code (0).

Printing “Hello world” in Java:

Explanation of above Java code:

• public class HelloWorld: This keyword defines a public class named HelloWorld .
• public static void main(String[] args): This declares the main function, which is the entry point of the program.
• System.out.println(“Hello, world!”); This statement prints the string “ Hello, world! ” to the console.

Printing “Hello world” in Python:

Explanation of above Python code:

• print: This keyword prints the following argument to the console.

Printing “Hello world” in Javascript:

Explanation of above Javascript code:

• console.log: This object’s method prints the following argument to the console.

Printing “Hello world” in PHP:

Explanation of above PHP code:

• <?php: This tag initiates a PHP code block.
• echo: This keyword prints the following expression to the console.
• ?>: This tag ends the PHP code block.

7. Top 20 Programs to get started with Coding/Programming Tutorial:

Here are the list of some basic problem, these problems cover various fundamental programming concepts. Solving them will help you improve your coding skills and understanding of programming fundamentals.

8. Next Steps after learning basic Coding/Programming Tutorial:

Congratulations on taking the first step into the exciting world of programming! You’ve learned the foundational concepts and are ready to explore more. Here’s a comprehensive guide to help you navigate your next steps:

A. Deepen your understanding of Basic Programming Concepts:

• Practice regularly: Implement what you learn through practice problems and coding exercises.
• Solve code challenges: Platforms like GeeksforGeeks, HackerRank, LeetCode, and Codewars offer challenges to improve your problem-solving skills and coding speed.

B. Learn advanced concepts:

• Data structures: Learn about arrays, linked lists, stacks, queues, trees, and graphs for efficient data organization.
• Algorithms: Explore algorithms for searching, sorting, dynamic programming, and graph traversal.
• Databases: Learn SQL and NoSQL databases for data storage and retrieval.
• Version control: Use Git and GitHub for code versioning and collaboration.

C. Choose a focus area:

• Web development: Learn HTML, CSS, and JavaScript to build interactive web pages and applications.
• Mobile app development: Choose frameworks like Flutter (Dart) or React Native (JavaScript) to build cross-platform apps.
• Data science and machine learning: Explore Python libraries like NumPy, pandas, and scikit-learn to analyze data and build machine learning models.
• Game development: Learn game engines like Unity (C#) or Unreal Engine (C++) to create engaging games.
• Desktop app development: Explore frameworks like PyQt (Python) or C# to build desktop applications.
• Other areas: Explore other areas like robotics, embedded systems, cybersecurity, or blockchain development based on your interests.

D. Build projects:

• Start with small projects: Begin with simple projects to apply your knowledge and gain confidence.
• Gradually increase complexity: As you progress, tackle more challenging projects that push your boundaries.
• Contribute to open-source projects: Contributing to open-source projects is a great way to learn from experienced developers and gain valuable experience.
• Showcase your work: Create a portfolio website or blog to showcase your skills and projects to potential employers or clients.

9. Resources and Further Learning

A. Online Courses and Tutorials:

• Interactive platforms: GeeksforGeeks, Codecademy, Coursera, edX, Khan Academy
• Video tutorials: GeeksforGeeks, YouTube channels like FreeCodeCamp, The Coding Train, CS50’s Introduction to Computer Science
• Language-specific tutorials: GeeksforGeeks, Official documentation websites, blogs, and community-driven resources

B. Books and eBooks:

• Beginner-friendly books: “Python Crash Course” by Eric Matthes, “Head First Programming” by David Griffiths
• A dvanced topics: “Clean Code” by Robert C. Martin, “The Pragmatic Programmer” by Andrew Hunt and David Thomas
• Free ebooks: Many free programming ebooks are available online, such as those on Project Gutenberg

C. Programming Communities and Forums:

• Stack Overflow: Q&A forum for programming questions
• GitHub: Open-source platform for hosting and collaborating on code projects
• Reddit communities: r/learnprogramming, r/python, r/webdev
• Discord servers: Many languages have dedicated Discord servers for discussions and support

D. Tips for Staying Motivated and Learning Effectively:

• Set realistic goals and deadlines.
• Start small and gradually increase complexity.
• Practice regularly and code consistently.
• Find a learning buddy or group for accountability.
• Participate in online communities and forums.
• Take breaks and avoid burnout.
• Most importantly, have fun and enjoy the process

10. Frequently Asked Questions (FAQs) on Programming Tutorial:

Question 1: how to learn programming without tutorial.

Answer: Learning programming without tutorials involves a self-directed approach. Start by understanding fundamental concepts, practicing regularly, and working on small projects. Utilize books, documentation, and online resources for reference.

Question 2: How to learn coding tutorial?

Answer: Learning coding through tutorials involves choosing a programming language, finding online tutorials or courses, and following them step by step. Practice coding alongside the tutorial examples and apply the concepts to real-world projects for a hands-on learning experience.

Question 3: What are 3 important things to know about programming?

Answer: Problem Solving: Programming is fundamentally about solving problems. Logic and Algorithms: Understanding logical thinking and creating efficient algorithms is crucial. Practice: Regular practice and hands-on coding improve skills and understanding.

Question 4: How many days do I need to learn programming?

Answer: The time to learn programming varies based on factors like prior experience, the complexity of the language, and the depth of knowledge desired. Learning the basics can take weeks, but mastery requires continuous practice over months.

Question 5: Can tutorials help coding?

Answer: Yes, tutorials are valuable resources for learning coding. They provide structured guidance, examples, and explanations, making it easier to understand and apply Programming Tutorial concepts.

Question 6: How do you use tutorials effectively?

Answer: Use tutorials effectively by following these steps: Set clear learning goals. Work on hands-on exercises and projects. Seek additional resources for deeper understanding. Regularly review and practice concepts learned.

Question 7: Can coding be done on a phone?

Answer: Yes, coding can be done on a phone using coding apps or online platforms that provide mobile-friendly coding environments. However, a computer is generally more practical for extensive coding tasks.

Question 8: Can I learn coding on GeeksforGeeks?

Answer: Yes, GeeksforGeeks is a popular platform for learning coding. Many Tutorials, Courses are provided you to learn various programming languages and concepts.

Question 9: Can we do coding on a laptop?

Answer: Yes, coding can be done on a laptop. Laptops are common tools for coding as they provide a portable and versatile environment for writing, testing, and running code.

Question 10: What is the difference between coding and programming?

Answer: The terms are often used interchangeably, but coding is typically seen as the act of writing code, while programming involves a broader process that includes problem-solving, designing algorithms, and implementing solutions. Programming encompasses coding as one of its stages.

This comprehensive programming tutorial has covered the fundamentals you need to start coding. Stay updated with emerging technologies and keep practicing to achieve your goals. Remember, everyone starts as a beginner. With dedication, you can unlock the world of programming!

Please Login to comment...

Similar reads.

• Programming

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

Computer Science (CSCI)

Explanation of course numbers.

• Courses in the 1000s are primarily introductory undergraduate courses
• Those in the 2000s to 4000s are upper-level undergraduate courses that also may be taken for graduate credit with permission and additional work assigned
• Those in the 6000s and 8000s are for master’s, doctoral, and professional-level students
• The 6000s are open to advanced undergraduate students with approval of the instructor and the dean or advising office

CSCI 1010. Computer Science Orientation. 1 Credit.

Introduction to the field of computer science. Basic and emerging concepts and applications of computer science. Hands-on experiments and team projects. Technical resources, professional ethics, writing, and presentation.

CSCI 1011. Introduction to Programming with Java. 3 Credits.

An introductory course in programming a computer, using the Java language. Object-oriented programming, classes, applets, methods, control structures, inheritance, overriding, GUI widgets, containers, and exceptions.

CSCI 1012. Introduction to Programming with Python. 3 Credits.

Introduction to programming a computer using the Python language; variables, types, assignment, conditionals, loops, lists, and program units. (Fall, spring, and summer, Every year)

CSCI 1013. Computational Problem-Solving Across Disciplines. 3 Credits.

Data structures, data types, visualizations relevant to interdisciplinary computing and data science applications. Computational problem-solving with applications in humanities, social sciences, and STEM. Prerequisites: CSCI 1012 .

CSCI 1020. Applications Software. 3 Credits.

Introduction to the use of microcomputer hardware and software for word processing (e.g., Word), spreadsheets (e.g., Excel), and database management (e.g., Access), with emphasis on the use of computers to solve typical problems in academia and business.

CSCI 1021. Introduction to Computers and the Internet. 3 Credits.

Survey of computers and languages. Introduction to computer programming. History of computing and networking. The effects of computing and the Internet on our lives. E-commerce and new technologies. Concepts of web page design.

CSCI 1022. Introduction to Internet Technology. 3 Credits.

An introductory course for non-technical students who wish to obtain a better understanding of the hardware and software that comprise the Internet. Information transfer over fiber, routing and switching of packets, methods of information transfer, protocols, software, ISP, web pages and multimedia.

CSCI 1023. Introduction to Web Software Development. 3 Credits.

Introduction to the Internet. Topics include address and URL to find your way, linking to a URL, HTML and web programming, building a web page, building a home page, client-server techniques.

CSCI 1030. Technology and Society. 3 Credits.

Historical, social, and ethical issues of the technological age. Ethical principles and skills and social analysis skills needed to evaluate the design and implementation of complex computer systems. Privacy, computer crime, equity, intellectual property, professional ethics. Data collection, analysis, and presentation; technical writing and oral communication skills.

CSCI 1030W. Technology and Society. 3 Credits.

Historical, social, and ethical issues of the technological age. Ethical principles and skills and social analysis skills needed to evaluate the design and implementation of complex computer systems. Privacy, computer crime, equity, intellectual property, professional ethics. Data collection, analysis, and presentation; technical writing and oral communication skills. Includes a significant engagement in writing as a form of critical inquiry and scholarly expression to satisfy the WID requirement. (Fall and spring, Every year)

CSCI 1041. Introduction to FORTRAN Programming. 3 Credits.

Structured programming with high-level language using FORTRAN. Control structures. Different data types with emphasis on real and complex number computations. Arrays used with vector and matrix manipulation to solve simultaneous equations. External subroutines for mathematical and graphical applications. MATH 1220 or MATH 1231 may be taken as a corequisite. Prerequisites: MATH 1220 or MATH 1231 . (Fall and spring, Every year)

CSCI 1099. Variable Topics. 1-36 Credits.

CSCI 1111. Introduction to Software Development. 3 Credits.

Introduction to problem solving on a digital computer using the Java language. Object-oriented programming concepts; documentation techniques; design of test data. Writing, debugging, and running programs in an interactive computing environment. (Spring, summer, and fall, Every year)

CSCI 1112. Algorithms and Data Structures. 3 Credits.

Object-oriented software. Inheritance, exceptions, development of classes, event-driven programming. Data structures such as trees, lists, stacks, queues, and strings. Sorting and searching. Introduction to algorithm performance prediction. Prerequisites: CSCI 1111 with a minimum grade of C; and MATH 1220 or MATH 1231 . (Spring, Every year)

CSCI 1121. Introduction to C Programming. 3 Credits.

Structured programming with the C language; control structures; data types; use of pointers; matrix manipulation to solve simultaneous equations; external subroutines for mathematical and graphical applications; introduction to C++; complex number representation. Co-requisites: MATH 1220 and MATH 1231 . Credit cannot be earned for this course and ECE 1120 . (Spring, Every year)

CSCI 1131. Introduction to Programming with C. 3 Credits.

Intensive introductory course for students with a science, mathematics, or other quantitative backgrounds. Solution of numerical and nonnumerical problems on a digital computer using C programming language in a Unix environment. Prerequisites: MATH 1232 .

CSCI 1132. Data Structures and Software Design. 3 Credits.

Data structures such as trees, lists, stacks, queues, and strings. Big-O notation and introduction to algorithm performance analysis. Solutions of numerical and non-numerical problems. Use of I/O libraries. Application development and software testing. Prerequisite: CSCI 1121 .

CSCI 1311. Discrete Structures I. 3 Credits.

Mathematics for computer science. Sets, functions, sequences. Propositional and predicate calculus, formal proofs, mathematical induction. Matrices, semigroups, groups, isomorphism. Relations, partitions, equivalence relations, trees, graphs. Prerequisites: MATH 1220 or MATH 1231 . (Fall, spring, summer, Every year)

CSCI 2113. Software Engineering. 3 Credits.

Programming techniques and software development in one or more programming languages; application development using object oriented programming techniques, GUIs, threading, and networking while applying software engineering principles. Prerequisites: MATH 1221 or MATH 1231 ; and CSCI 1112 with a minimum grade of C. (Spring and fall, Every year)

CSCI 2211. Computing and the World. 3 Credits.

The role played by modern computing in the significant social, political, economic, and climate challenges the world faces, relying on perspective provided by other relevant fields. Restricted to students in the BS in computer science program. Prerequisites: CSCI 1112 ; and one of the following: APSC 3115 , CSCI 3362 , CSCI 6362 , CSCI 4341 , or STAT 4157 , prior coursework in programming and data structures and sufficient knowledge of probability/statistics to understand the explanation of some estimation algorithms. (Spring, Every year)

CSCI 2312. Discrete Structures II. 3 Credits.

Basic discrete techniques in computer science; proofs, algebraic structures, number theory, graph theory, asymptotics. Prerequisites: MATH 1221 or MATH 1231 or MATH 1232 or MATH 2233 ; and CSCI 1311 with a minimum grade of C. (Fall, Every year)

CSCI 2410. System Programming. 3 Credits.

Systems programming on POSIX systems; C programming, programs, the command line, processes, input/output, file system access, inter-process communication, libraries, system calls, memory management, injection and overflow attacks, security. Restricted to students in the computer science program. Prerequisites: CSCI 1112 with a minimum grade of C. Credit cannot be earned for this course and CSCI 3410 . (Fall, Every year)

CSCI 2441. Database Systems and Team Projects. 3 Credits.

Design of relational database systems, relational query languages, normal forms, and design of database applications. Team software development, integration, and testing. Professional code of ethics, intellectual property, privacy, and software copyrights. Students cannot receive credit for both CSCI 2441 taken while an undergraduate and CSCI 6441 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 2541 and CSCI 6441 . (Fall and spring, Every year)

CSCI 2441W. Database Systems and Team Projects. 3 Credits.

Design of relational database systems, relational query languages, normal forms, and design of database applications. Team software development, integration, and testing. Professional code of ethics, intellectual property, privacy, and software copyrights. Students cannot receive credit for both CSCI 2441W taken while an undergraduate and CSCI 6441 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 2441W and CSCI 6441 . Includes a significant engagement in writing as a form of critical inquiry and scholarly expression to satisfy the WID requirement. Corequisite: CSCI 2113 . (Spring, Every year)

CSCI 2460. Introduction to Computer Systems. 3 Credits.

Introduction to computer organization and computer systems. Data representation; computer organization; processor instruction sets and assembly programming; language translation; memory; input/output; introduction to operating systems. Restricted to students in the BA and minor programs in computer science. Prerequisites: CSCI 1112 and CSCI 1311 with a minimum grade of C. Corequisites: CSCI 2113 . Recommended background: Introductory coursework and background in computer science, programming, and discrete mathematics. Credit cannot be earned for this course and CSCI 2461 . (Spring, Every year)

CSCI 2461. Computer Architecture I. 3 Credits.

Introduction to design and analysis of computer architectures. Number representation, digital logic and circuit design, computer organization, assembly programming, translating C programs, memory design, program performance. Restricted to students in the BS in computer science program. Prerequisites: CSCI 1112 and CSCI 1311 , both with a minimum grade of C. Corequisites: CSCI 2113 . Credit cannot be earned for this course and CSCI 2460 . (Fall, Every year)

CSCI 2501. Ethical Issues in Computing. 1 Credit.

Introduction and analysis of the ethical issues of the technological age; ethical principles and skills and social analysis skills needed to evaluate future consequences of the design and implementation of complex computer systems; application of professional ethics codes in decision-making in professional practice. Restricted to computer science majors. Prerequisites: CSCI 1010 and CSCI 1011 . (Fall and spring, Every year)

CSCI 2541W. Database Systems and Team Projects. 3 Credits.

Design of relational database systems, relational query languages, Introduction to Not just SQL (NoSQL) database systems, normal forms, and design of database applications. Team software development, integration, and testing. Students cannot receive credit for both CSCI 2541W taken while an undergraduate and CSCI 6441 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 2541 and CSCI 6441 . Includes a significant engagement in writing as a form of critical inquiry and scholarly expression to satisfy the WID requirement. Corequisite: CSCI 2113 . Prerequisite: CSCI 1311 . (Spring, Every year)

CSCI 3212. Algorithms. 4 Credits.

Core concepts in design and analysis of algorithms, data structures, and problem-solving techniques. Hashing, heaps, trees. Graph algorithms, searching, sorting, graph algorithms, dynamic programming, greedy algorithms, divide and conquer, backtracking. Combinatorial optimization techniques. NP-completeness. Prerequisites: CSCI 1311 and CSCI 2113 . (Fall and spring, Every year)

CSCI 3313. Foundations of Computing. 3 Credits.

Introduction to the theory of computing and automata theory. Finite state automata, regular expressions, context-free languages; pushdown automata; Turing machines and computability. Prerequisites: CSCI 2113 ; and CSCI 2312 or CSCI 2460 or CSCI 2461 . (Spring, Every year)

CSCI 3362. Probability for Computer Science. 3 Credits.

Introduction to probability and statistics for computer scientists; random variables; conditional probability, independence, correlation; applications to computer science, including information theory, data compression, coding, inference, Markov chains, randomized algorithms. Students cannot receive credit for both CSCI 3362 taken while an undergraduate and CSCI 6362 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 3362 and CSCI 6362 . Prerequisites: CSCI 1311 and MATH 1232 . (Spring, Every year)

CSCI 3401. Computer Architecture and Organization. 3 Credits.

Design and analysis of computer architectures. Digital logic and circuit design, computer organization, assembly programming, translation, memory design, pipelined and superscalar architectures, multiprocessing, program performance. Restricted to students in the BS in computer science program. Prerequisites: CSCI 1311 , CSCI 2113 , and CSCI 2410 . Credit cannot be earned for this course and CSCI 2461 . (Spring, Every year)

CSCI 3410. Systems Programming. 3 Credits.

Concepts underlying all computer systems. Processor operation, hierarchical memory systems, embedded boards, data acquisition, actuation, and systems software such as compilers, linkers, and operating systems from the programmer’s perspective. Use of embedded platforms to examine how programs interact with and are constrained by hardware. Prerequisites: CSCI 2461 and CSCI 2113 . (Fall and spring, Every year)

CSCI 3411. Operating Systems. 4 Credits.

Operating systems structure and associated trade-offs; process and thread management; concurrency, synchronization, and deadlock; memory management; scheduling; storage systems and I/O. Prerequisites: CSCI 2113 ; and CSCI 2461 or CSCI 2410 . (Fall, Every year)

CSCI 3462. Computer Architecture II. 3 Credits.

Computer organization; design of computer components and of a simple computer. Instruction set and assembly language of a pipelined RISC processor; introduction to high-performance processors; design of cache, main memory, and virtual memory systems; program performance models and system performance; I/O structure and peripherals. Prerequisites: CSCI 2113 and CSCI 2461 . Credit cannot be earned for this course and ECE 3515 . (Spring, Every year)

CSCI 3532. Information Ethics and Policy. 3 Credits.

Ethical issues of new technologies viewed through ethical frameworks. Developing policy analysis skills to evaluate local, global impact of complex computer systems and applying professional ethics codes in decision making in professional practice. Restricted to students in the BS in computer science program. Prerequisites: CSCI 1010 , or CSCI 1011 , or CSCI 1111 . (Spring, Every year)

CSCI 3551. Concepts and Applications of Computer Graphics. 3 Credits.

Introduction to computer graphics without programming; building 3-D geometry and rendering; computer animation; virtual reality and computer games; hands-on projects in modeling, rendering, and animation using commercial software.

CSCI 3552. Design of Computer Animation I. 3 Credits.

Use of commercial 3-D computer animation packages to create digital artistic works. Principles of animation, including timing, exaggeration of motion, and anticipation; use of a storyboard; modeling; motion; rendering and editing. (Fall, Every year)

CSCI 3571. Introduction to Bioinformatics. 3 Credits.

The use of computational techniques in molecular biology, genetics, and evolution; techniques and software for database searching, sequence alignment, gene finding, phylogenetics, genomics, and proteomics. Prerequisites: BISC 1111 and BISC 1112 . Same As: BISC 2584 .

CSCI 3907. Special Topics. 1-3 Credits.

Topic to be announced in the Schedule of Classes.

CSCI 3908. Research. 1-3 Credits.

Applied research and experimentation projects, as arranged. Restricted to juniors and seniors. (Fall and spring, Every year)

CSCI 4223. Principles of Programming Languages. 3 Credits.

Fundamental concepts underlying design of programming languages. Detailed study of functional and object-oriented computational models. Types, evaluation, abstraction, control flow, modules, mutation, laziness, polymorphism, subtyping, inheritance. Practice learning new languages. Students cannot receive credit for both CSCI 4223 taken while an undergraduate and CSCI 6223 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4223 and CSCI 6223 . Prerequisites: CSCI 1311 and CSCI 2113 . (Spring, Odd years)

CSCI 4235. Development of Open-Source Software. 3 Credits.

Design, process, tools, and culture of open-source software development. Cross-platform development and testing. Geographic dispersal, social and team dynamics, licenses (GPL, BSD, other); code reuse (modular code, shared libraries); very-large-scale distributed development techniques (CVS, Bugzilla, release-management, mailing-lists). May be taken for graduate credit. Prerequisite: CSCI 2113 or CSCI 6221 .

CSCI 4237. Software Design for Handheld Devices. 3 Credits.

Design of interactive software for handheld devices. Event driven programming, user interface design practices, memory management, handheld debugging techniques. May be taken for graduate credit. Prerequisite: CSCI 2113 or CSCI 6221 .

CSCI 4243. Capstone Design Project I. 3 Credits.

Planning, design, and construction of the capstone project. Economic analysis of the project. Application of software engineering principles, including software requirements, specification, requirements engineering, reuse, documentation, verification/validation, testing, configuration management. Report writing and presentations. Prerequisite: senior status.

CSCI 4243W. Capstone Design Project I. 4 Credits.

Planning, design, and construction of the capstone project; economic analysis of the project; application of software engineering principles, including software requirements, specification, requirements engineering, reuse, documentation, verification/validation, testing, configuration management. Includes a significant engagement in writing as a form of critical inquiry and scholarly expression to satisfy the WID requirement. Prerequisites: CSCI 3212 and CSCI 3411 . (Fall, Every year)

CSCI 4244. Capstone Design Project II. 4 Credits.

Continuation of CSCI 4243 . ​Planning, design, and construction of the capstone project. Economic analysis of the project. Application of software engineering principles. Restricted to seniors. Prerequisites: CSCI 4243 or CSCI 4243W . (Spring, Every year)

CSCI 4331. Cryptography. 3 Credits.

Introduction to modern cryptography with a focus on formal definitions and provably-secure constructions. Topics include secret-key and public-key encryption, message-authentication codes, digital signatures, and advanced topics. Prerequisites: One of the following: CSCI 2312 , CSCI 3212 , CSCI 3313 , MATH 2971 , or MATH 2971W . Credit cannot be earned for this course and CSCI 6331 . (Fall, Every year)

CSCI 4341. Continuous Algorithms. 3 Credits.

Structures in continuous mathematics from a computational viewpoint; continuous system simulation, computational modeling, probability, statistical techniques, next-event simulation, algorithms for continuous optimization, machine learning, neural networks, statistical language processing, robot control algorithms. Students cannot receive credit for both CSCI 4341 taken while an undergraduate and CSCI 6341 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4341 and CSCI 6341 . Prerequisites: CSCI 1311 and CSCI 2113 . (Spring, Every year)

CSCI 4342. Computational Linear Algebra and Applications. 3 Credits.

Application of linear algebra to computer science and engineering, with a computational perspective; points, vectors, matrices, and their programming repreentations; algorithms for 3D transformations, pose and viewpoint estimation; linear equations, independence, rank; algorithms for matrix decompositions, reduction of dimension; computation with large matrices, under and over-determined systems; applications to large data, computer vision, text processing. Students cannot receive credit for both CSCI 4342 taken while an undergraduate and CSCI 6342 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4342 and CSCI 6342 . Prerequisite: CSCI 2113 . (Spring, Every year)

CSCI 4345. Introduction to Quantum Computing. 3 Credits.

Foundations of quantum computing, Dirac notation, complex vector spaces, state representation, operators. Single and multiple qubits, entanglement, measurement. Standard circuit model. Quantum algorithms and protocols, EPR paradox, Bell’s theorem. Prerequisite: CSCI 4342 , or EMSE 2705 , or MATH 2184 , or MATH 2185 ; and CSCI 3212 , or ECE 3220 , or MATH 2971 , or MATH 2971W , or PHYS 2023 . Credit cannot be earned for this course and CSCI 6345 . (Spring, Every year)

CSCI 4364. Machine Learning. 3 Credits.

Supervised and unsupervised learning, regression, classification, linear models, perceptrons, ensemble methods, non-parametrics, online learning, active learning, feature selection, parameter tuning, and model evaluation. Prerequisites: CSCI 3212; and CSCI 3362 or CSCI 4341 or CSCI 6362 or APSC 3115 or DNSC 2001 or STAT 4157 ; and CSCI 4342 or EMSE 2705 or MATH 2184 or MATH 2185 . Recommended background: Algorithms, probability and statistics, and linear algebra. Credit cannot be earned for this course and CSCI 6364 . (Spring and fall, Every year)

CSCI 4366. Neural Networks and Deep Learning. 3 Credits.

Introduction to deep learning with neural networks: theory and practice. Creating from scratch, simplification with software packages, regression and classification problems for both simple (numerical, etc.) and complex (images, etc.) datasets. Restricted to Computer science majors. Prerequisites: MATH 1231 and CSCI 3212 ; and MATH 2184 or MATH 2185 or CSCI 4342 or EMSE 2705 . Recommended background: Calculus, linear algebra, programming, and algorithms. Credit cannot be earned for this course and CSCI 6366 . (Fall, Every year)

CSCI 4414. Introduction to Blockchain Technology and Applications. 3 Credits.

Introduction to blockchain concepts. Illustrates applications in both technical and business contexts. May be taken for graduate credit. Recommended background: prior coursework and background in computer science disciplines, programming, security protocols, machine learning, and distributed systems is helpful. (Fall and spring, Every year)

CSCI 4415. Real-Time and Embedded Systems. 3 Credits.

Development of software for real-time control of physical systems; reliability and fault tolerance, exceptions and exception handling, reliability and concurrent processes, timeouts, deadline scheduling, shared-memory and message-based device drivers. May be taken for graduate credit. Prerequisite: CSCI 2113 . (Spring, Every year)

CSCI 4417. UNIX System Programming. 3 Credits.

Exposure to UNIX internals. Use of UNIX system calls and utilities in conjunction with script and C programs. RFCs, GNU project, and other collaborative traditions in the UNIX community. May be taken for graduate credit. Prerequisite: Senior status or 1 year of C programming and UNIX user experience.

CSCI 4418. UNIX System Administration. 3 Credits.

System administration for the stand-alone system or small networks. Installation of two or more UNIX variants (Linux, FreeBSD, Solaris) on Intel or Sparc platforms. Configuration of mail, name services, and other network utilities. Backup and recovery, security and ethics. May be taken for graduate credit. Prerequisite: CSCI 4417 .

CSCI 4431. Computer Networks I. 3 Credits.

Higher-layer protocols and network applications on the Internet, such as session layer, presentation layer, data encryption, directory services and reliable transfer services, telnet, network management, network measurements, e-mail systems, and error reporting. Students cannot receive credit for both CSCI 4431 taken while an undergraduate and CSCI 6431 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4431 and CSCI 6431 . Prerequisites: CSCI 2113 and CSCI 2461 . (Fall, Every year)

CSCI 4431W. Computer Networks I. 3 Credits.

Higher-layer protocols and network applications on the Internet, such as session layer, presentation layer, data encryption, directory services and reliable transfer services, telnet, network management, network measurements, e-mail systems, and error reporting. Includes a significant engagement in writing as a form of critical inquiry and scholarly expression to satisfy the WID requirement. Prerequisites: CSCI 2113 and CSCI 2461 . (Fall and spring, Every year)

CSCI 4454. Augmented and Virtual Reality. 3 Credits.

Principles and practice of augmented reality (AR) and virtual reality (VR); 3D UI/UX and user interaction for immersive experiences; design and implementation of VR and mobile AR applications. May be taken for graduate credit with extra work. Prerequisites: CSCI 2113 . (Spring and fall, Every year)

CSCI 4455. Computer Game Design and Programming. 3 Credits.

Principles, techniques, and design of computer games. Graphic game engines, modeling, motion, AI and interaction; sound design and synthesis; real-time software and hardware issues. May be taken for graduate credit with extra work assigned. Prerequisites: CSCI 2113 .   (Every Year)

CSCI 4511. Artificial Intelligence Algorithms. 3 Credits.

Knowledge representation and reasoning, propositional logic and predicate calculus. Logic programming; search, game trees, backtracking; planning. May be taken for graduate credit. Prerequisites: CSCI 3212 and CSCI 3221. (Spring, Every year)

CSCI 4521. Introduction to Mobile Robotics. 3 Credits.

Overview of autonomous mobile robotics. Sensing, localization, calibration, mapping, perception, decision making, planning, and control. Emphasis on algorithmic rather than hardware aspects of robotics. Development of algorithms that can operate autonomous mobile platforms in complex, real-world environments. Prerequisites: MATH 1232 and MATH 2184 ; and CSCI 3362 or CSCI 4341 . (Fall and spring, Every year)

CSCI 4525. Autonomous Robotics: Manipulation. 3 Credits.

Introduction to robot manipulation. Core principles necessary to program robots for autonomous operation in dynamic and typically human-centric environments. Transdisciplinary concepts from computer science (reinforcement learning, perception), mechanical engineering (kinematics, dynamics), and electrical engineering (control theory). Prerequisites: Permission of the instructor. (Fall and spring, Every year)

CSCI 4527. Introduction to Computer Vision. 3 Credits.

Introduction and overview of computer vision. Image-formation signal processing and filtering. Saliency, image features and feature extraction, tracking, stereo disparity estimation, structure form motion, photogrammetry, optic flow, homography estimation and warping, scene segmentation, place recognition, object recognition, robust estimation, and camera calibration. Prerequisites: MATH 1232 and MATH 2184 ; and CSCI 3362 or CSCI 4341 . Same As: CSCI 6527 . (Spring, Every year)

CSCI 4531. Computer Security. 3 Credits.

Risk analysis, cryptography, operating system security, identification and authentication systems, database security. Students cannot receive credit for both CSCI 4531 taken while an undergraduate and CSCI 6531 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4531 and CSCI 6531 . Corequisite: CSCI 4331 . Prerequisite: CSCI 3411 . (Spring, Every year)

CSCI 4532. Information Policy. 3 Credits.

Roles, issues, and impacts of computer-based information systems in national and international arenas, focusing on privacy, equity, freedom of speech, intellectual property, and access to personal and governmental information. Professional responsibilities, ethics, and common and best practices in information use. Students cannot receive credit for both CSCI 4532 taken while an undergraduate and CSCI 6532 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4532 and CSCI 6532 . (Fall, Every year)

CSCI 4533. Introduction to Usable Security and Privacy. 3 Credits.

Introduction to concepts and research methods in human factors in computer security and privacy; reading, writing, reviewing scientific literature; developing research problems for exploration and presentation. Restricted to computer science majors and minors. Prerequisites: CSCI 2113 . (Spring, Every year)

CSCI 4541. Network Security. 3 Credits.

Security protocols and applications in local, global, and wireless networks; IPSec and packet-level communication security systems; network authentication and key-exchange protocols; intrusion detection systems and firewalls; secure network applications; network worms and denial-of-service attacks. Students cannot receive credit for both CSCI 4541 taken while an undergraduate and CSCI 6541 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4541 and CSCI 6541 . Prerequisite: CSCI 4531 . (Spring, Every year)

CSCI 4551. Concepts and Applications of Computer Graphics. 3 Credits.

Introduction to computer graphics without programming; building 3-D geometry and rendering; computer animation; virtual reality and computer games; hands-on projects in modeling, rendering, and animation using commercial software; hands-on projects in photo and video manipulation.

CSCI 4552. Design of Computer Animation I. 3 Credits.

CSCI 4553. Design of Computer Animation II. 3 Credits.

Use of commercial 3-D animation packages to create artistic works and visualizations. Process-spanning concepts of development through pre-production, production, and post-production. Emphasis on developing original content and attaining high production values. Prerequisite: CSCI 4552 .

CSCI 4554. Computer Graphics I. 3 Credits.

Graphics primitives; 2D, 3D, and viewing transformations; hierarchical modeling and animation; illumination and shading; texture mapping; shaders; visibility and collision detection; sampling and anti-aliasing; global illumination; projects using OpenGL graphics API. May be taken for graduate credit. Prerequisites: CSCI 2113 or CSCI 6221 . (Spring, Every year)

CSCI 4561. Design of User–Interface Programs. 3 Credits.

Structure of interactive programs. Widgets, windows, and input devices. Client–server model, event-driven programming, and callbacks. Window systems (e.g., Xwindows) and dialog control. May be taken for graduate credit. Prerequisite: CSCI 2113 or CSCI 6221 .

CSCI 4572. Computational Biology. 3 Credits.

Pairwise alignment and scoring. Multiple sequence alignment. Fragment assembly, physical mapping of DNA. Phylogenetic trees. Molecular structure prediction and protein folding. Microarrays and microarray data, image comparison. Clustering. Overview of biological databases, PDB, MMDB, GenBank. Draft genomes and genome browsers. Pathway databases. May be taken for graduate credit. Prerequisite: CSCI 3571 and CSCI 3212 or CSCI 6212 .

CSCI 4576. Introduction to Biomedical Computing. 3 Credits.

A survey of the problems and solutions in biomedical computing. Application of computers in medicine. Patient care and monitoring systems, electronic medical records, digital imaging and analysis. Telemedicine, medical ethics, health care regulations and organizations.

CSCI 4577. Biomedical Computing. 3 Credits.

Computing issues in epidemiology and biosurveillance, decision support, medical imaging and visualization, image-guided surgery; medical databases, issues in system integration, mobile medical computing. May be taken for graduate credit. Corequisite: CSCI 2441 . Restricted to graduate students. Prerequisites: CSCI 2113 and CSCI 4576 . (Spring, Every year)

CSCI 4907. Advanced Topics. 3 Credits.

Topics vary by semester. May be repeated for credit provided topic differs. Consult the Schedule of Classes for more information. Restricted to computer science majors and minors. (Spring and fall, Every year)

CSCI 5099. Variable Topics. 1-99 Credits.

CSCI 6001. Introduction to Computer Programming and Software Development. 3 Credits.

Introduction to concepts and skill development in programming and software development, including problem solving on a digital computer and writing, debugging, and executing programs. Restricted to students in select programs; departmental permission is required. (Fall, spring, and summer, Every year)

CSCI 6002. Introduction to Data Structures and Their Applications. 3 Credits.

Introduction to core computer science data structures including: arrays, lists, linked structures, stacks, queues, and trees. Sorting, searching, and comparison of algorithmic performance. Restricted to students in select programs; departmental permission is required. (Fall, spring, and summer, Every year)

CSCI 6003. Introduction to Software Design and Engineering. 3 Credits.

Introduction to objects and object-oriented programming. Software development for applications including development with GUIs, database access, threads, web programming. Restricted to students in select programs; departmental permission is required. (Fall, spring, and summer, Every year)

CSCI 6004. Introduction to Web Development. 3 Credits.

Client-server programming, web development, front end design, back-end server development, database use for web services. Front and back-end languages, server administration and tools. Departmental permission required prior to enrollment. Prerequisites: CSCI 2113 or CSCI 6003 . Credit cannot be earned for this course and CSCI 2441 , CSCI 2441W , CSCI 2541W .

CSCI 6010. Introduction to Computer Science Fundamentals. 3 Credits.

Review of programming in a high-level language using Java or C++ Introduction to objects and object-oriented programming: static and dynamic objects, inheritance, dynamic method invocation. Data structures: 2D-arrays, linked-lists, stacks, queues, trees, hashing. Discrete structures: sets, graphs, permutations and combinations. Restricted to students whose letter of admission stated that the course is required. (Fall and spring, Every year)

CSCI 6011. Introduction to Computer Systems. 3 Credits.

Introduction to basic concepts underlying all computer systems; processor operation, hierarchical memory systems, elementary logic circuits, and systems software such as compilers, linkers, and operating systems from the programmer’s perspective. Students must have completed one year of coursework in programming in C, C++, or Java prior to registration. Restricted to students whose letter of admission stated that the course is required. (Fall and spring, Every year)

CSCI 6012. Cybersecurity and Privacy. 3 Credits.

Overview of cybersecurity and privacy, including cryptography, authentication, malware, viruses, network security, anonymity, privacy and online privacy, risk management; common cyberattacks and techniques for detection and defense; policy and legal perspectives for managing cybersecurity missions supporting private sector and government; cyber technologies as applied to the stability of global information and communications infrastructure; government cybersecurity policies. (Fall, spring, And summer)

CSCI 6013. Security in Mobile Computing. 3 Credits.

Relationship between security strategic plan and business strategic plan; mobile device solutions (MDS) to access enterprise corporate data; bring your own device (BYOD) paradigm; mobile device management (MDM) best practices, policies, network controls to identify countermeasures, and risk mitigation strategies against common threats. Overview of mobile security solutions for classified processing and communications. Prerequisites: CSCI 6012 . (Fall, spring, And summer)

CSCI 6015. Cyber Forensics. 3 Credits.

Acquiring, preserving and analyzing digitally stored information while ensuring that this information is admissible as evidence in a court of law. Principles and techniques for cyber forensics investigations using industry-standard forensic process. Restricted to SEAS online students.

CSCI 6016. Applied Network Defense. 3 Credits.

Computer security: protection aspects of the Internet. Cryptographic tools to provide security, such as shared key encryption (DES, 3DES, RC and more), public key encryption, key exchange, and digital signature. Internet protocols and applications. Restricted to SEAS online students.

CSCI 6018. Cloud Application Architecture. 3 Credits.

Cloud application design guidelines and software patterns. Survey of cloud services for scalable secure cloud applications. Trade-offs in cloud application design, container vs virtual machine deployments, and monolithic vs microservice. Restricted to SEAS online students.

CSCI 6114. Introduction to Computer Systems and Systems Programming. 3 Credits.

Introduction to basic concepts underlying all computer systems; processor operation, hierarchical memory systems, elementary logic circuits, and systems software such as compilers, linkers, and operating systems from the programmer’s perspective. Software development with the C programming language. Students cannot receive credit for this course and CSCI 6011 . Restricted to students in select programs; departmental permission is required. Prerequisites: CSCI 2113 or CSCI 6003 . (Fall, spring, and summer, Every year)

CSCI 6115. Application Development I. 3 Credits.

Client-server programming, web development, front end design, back-end server development, introduction to databases. Front and back-end languages, server administration and tools. Students cannot get credit for this course and CSCI 2441 , CSCI 2441W , CSCI 2541, or CSCI 2541W . Restricted to students in select programs; departmental permission is required. Prerequisites: CSCI 2113 or CSCI 6003 . (Fall, spring, and summer, Every year)

CSCI 6116. Advanced Application Development. 3 Credits.

Design of large software systems and installable applications, development frameworks, integration of components and services, cloud and web programming, and mobile device development; software specification and testing. Prerequisites: CSCI 6115 , CSCI 6431 and CSCI 6441 . (Fall, spring, and summer, Every year)

CSCI 6212. Design and Analysis of Algorithms. 3 Credits.

Design and analysis of algorithms; Turing machines; NP-complete theory; algorithmic techniques: divide-and-conquer, greedy, dynamic programming, graph traversal, backtracking, and branch-and-bound; applications include sorting and searching, graph algorithms, and optimization. Students are expected to know data structures and possess general programming skills in one or more procedural/OOP language such as C/C++/Java, and to have a good mathematical background such as discrete math and some calculus, prior to registration. (Fall, spring, and summer, Every year)

CSCI 6221. Advanced Software Paradigms. 3 Credits.

Object-oriented, procedural, functional, and concurrent software design paradigms; design patterns; software life cycle concepts; tadeoffs between compiled and interpreted languages; examples from Java, C, C++ and Perl. Restricted to graduate students. (Fall, spring, and summer, Every year)

CSCI 6223. Principles of Programming Languages. 3 Credits.

Fundamental concepts underlying design of programming languages; detailed study of functional and object-oriented computational models; types, evaluation, abstraction, control flow, modules, mutation, laziness, polymorphism, subtyping, inheritance. Students cannot receive credit for both CSCI 6223 taken while a graduate and CSCI 4223 taken while an undergraduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4223 and CSCI 6223 . (Spring, Odd years)

CSCI 6231. Software Engineering. 3 Credits.

The life-cycle model. Requirements and specifications. Design models, structured and object-oriented design. Program development, PDL’s tools, configuration control. Program, unit, and integration testing. Program verification. Other development models. Development metrics. Computer-aided software engineering (CASE). Prerequisites: CSCI 6221 and CSCI 6212 . (Fall and spring, Every year)

CSCI 6232. Software Engineering Development. 3 Credits.

Formal methods in software engineering. First-order logic, basic specification elements, rigorous proofs, formal development process, concurrency. Prerequisites: CSCI 6461 and CSCI 6212 . (Fall and spring, Every year)

CSCI 6233. Software Testing and Quality. 3 Credits.

Flow graphs and path testing, transaction flow testing, data flow testing, software metrics, system testing, test planning and documentation, reliability, statistical testing. Prerequisite: CSCI 6231 . (Fall and spring, Every year)

CSCI 6234. Object-Oriented Design. 3 Credits.

Object-oriented systems, software reusability, software modularity, top–down and bottom–up approaches, object classification, genericity, metaprogramming, concurrent object-oriented programming languages. Prerequisite: CSCI 6221 .

CSCI 6235. Component-Based Enterprise Software Development. 3 Credits.

Component-based software development for enterprise applications. Component models, multi-tier architecture. Specific case studies may include topics such as Enterprise Java Beans, DCOM, and COBRA. Prerequisite: CSCI 6221 .

CSCI 6311. Theory of Computation. 3 Credits.

Theoretical foundations of computer science. Formal languages and automata; regular expressions, context-free languages, parsing; Turing machines and complexity; partial recursive functions; undecidability; program correctness; fixed-point theory; formal specifications of software. Prerequisite: CSCI 6212 .

CSCI 6312. Graph Theory and Applications. 3 Credits.

Undirected and directed graphs. Connectivity, partitions, cycles and matchings. Edge and vertex coloring, chromatic polynomials, and the four-coloring problem. Planar graphs and Kuratowski’s theorem. Properties of random graphs. Applications to a variety of problems. Prerequisite: CSCI 6212 . (Fall and spring, Every year)

CSCI 6318. Complex Systems. 3 Credits.

The edge-of-chaos phenomenon, phase transitions, power laws, small-world networks, Boolean networks, cellular automata, and complex dynamics. Applications to networks and biological systems. Prerequisite: CSCI 6212 .

CSCI 6331. Cryptography. 3 Credits.

Introduction to modern cryptography with a focus on formal definitions and provably-secure constructions. Topics include secret-key and public-key encryption, message-authentication codes, digital signatures, and advanced topics. Prerequisites: CSCI 6212 . Credit cannot be earned for this course and CSCI 4331 . (Fall, Every year)

CSCI 6341. Continuous Algorithms. 3 Credits.

Structures in continuous mathematics from a computational viewpoint; continuous system simulation, computational modeling, probability, statistical techniques, next-event simulation, algorithms for continuous optimization, machine learning, neural networks, statistical language processing, robot control algorithms. Students cannot receive credit for both CSCI 4341 taken while an undergraduate and CSCI 6341 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4341 and CSCI 6341 . (Spring, Every year)

CSCI 6342. Computational Linear Algebra and Applications. 3 Credits.

Linear algebra applied to computational problems in computer science and engineering; points, vectors, matrices, and their programming abstractions; 3D transformations, pose and viewpoint estimation; linear equations; algorithms for matrix decompositions, dimension reduction, computation with large matrices, under- and over-determined systems; applications to big data, computer vision, text processing. Students cannot receive credit for both CSCI 4342 taken while an undergraduate and CSCI 6342 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4342 and CSCI 6342 . (Spring, Every year)

CSCI 6345. Introduction to Quantum Computing. 3 Credits.

Foundations of quantum computing, Dirac notation, complex vector spaces, state representation, operators. Single and multiple qubits, entanglement, measurement. Standard circuit model. Quantum algorithms and protocols, EPR paradox, Bell’s theorem. Recommended background: Linear algebra. Credit cannot be earned for this course and CSCI 4345 . (Spring, Every year)

CSCI 6351. Data Compression. 3 Credits.

Background on signals, information theory, transforms, human vision, and metrics. Lossless and lossy compression techniques. Video compression. Compression standards. Progressive transmission. Prerequisite: CSCI 6212 .

CSCI 6362. Probability for Computer Science. 3 Credits.

Concepts of probability and statistics used in computer science; random variables; conditional probability, independence, correlation; law of large numbers, central limit theorem; applications to computer science, including entropy, information theory, data compression, coding, inference, Markov chains, randomized algorithms. Students cannot receive credit for both CSCI 3362 taken while an undergraduate and CSCI 6362 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 3362 and CSCI 6362 . (Spring, Every year)

CSCI 6364. Machine Learning. 3 Credits.

Supervised and unsupervised learning, regression, classification, linear models, perceptrons, ensemble methods, non-parametrics, online learning, active learning, feature selection, parameter tuning, and model evaluation. Prerequisites: CSCI 6212 . Recommended background: Algorithms, Probability and Statistics and Linear Algebra. Credit cannot be earned for this course and CSCI 4364 . (Spring and fall, Every year)

CSCI 6365. Advanced Machine Learning. 3 Credits.

Theory and algorithms for machine learning research; in-depth focus on advanced machine learning topics such as clustering, learning from data streams, and climate informatics. Prerequisite: CSCI 6364 . (Spring, Every year)

CSCI 6366. Neural Networks and Deep Learning. 3 Credits.

Introduction to deep learning with neural networks: theory and practice. Creating from scratch, simplification with software packages, regression and classification problems for both simple (numerical, etc.) and complex (images, etc.) datasets. Restricted to computer science majors. Prerequisites: CSCI 6212 . Recommended background: Calculus, linear algebra, programming, and algorithms. Credit cannot be earned for this course and CSCI 4366 . (Fall, Every year)

CSCI 6411. Advanced Operating Systems. 3 Credits.

Fundamentals of operating system design and structure, resource management, and system support for multi-core. Topics include scheduling, synchronization, system structure, virtual address spaces, memory management, I/O management, and systems abstractions for modern multi-core architectures. The course involves an implementation component and requires substantial programming experience. This course can be taken for credit by undergraduates who have taken CSCI 3411 . Prerequisite: CSCI 6461 or CSCI 2461 .

CSCI 6412. OS Design and Implementation. 3 Credits.

Builds on CSCI 6411 to provide students with the knowledge to build parts of modern operating systems, which is studied and motivated from the viewpoint of practical design and implementation. Students learn how operating system’s components for resource management and abstraction are built from the ground up and integrated into working systems considering the challenges of reliability, multi-core, and security. The course has a significant implementation component; substantial low-level programming experience is required. Prerequisite: CSCI 6411 . (Fall and spring, Every year)

CSCI 6418. Unix Systems Administration. 3 Credits.

System administration for the stand-alone system or small networks; installation of two or more UNIX variants (Linux, FreeBSD, Solaris) hardware platforms; configuration of mail, name services, and other network utilities; backup and recovery, security and ethics. Students cannot receive credit for both CSCI 4418 taken while an undergraduate and CSCI 6418 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4418 and CSCI 6418 . Prerequisites: CSCI 6114 ; or CSCI 6010 and CSCI 6011 . (Fall, spring, and summer, Every year)

CSCI 6419. Advanced Systems Administration. 3 Credits.

Administration of large systems, non-Unix platforms, web document systems, website administration, cloud and web services, user and IT personnel components, and economics of IT support. Prerequisite: CSCI 6418 . (Fall, spring, and summer, Every year)

CSCI 6421. Distributed and Cluster Computing. 3 Credits.

Algorithmic and implementation challenges in building large scale distributed applications; distributed coordination, scheduling, consistency issues, and fault tolerance algorithms; fundamental distributed systems concepts applied to both high performance computing and cloud computing environments. Prerequisite: CSCI 6212 . Recommended background: Substantial programming experience. (Fall, Every year)

CSCI 6431. Computer Networks. 3 Credits.

Fundamental concepts in the design and implementation of computer communication networks and internet, their protocols, and applications; layered network architectures, applications, network programming interfaces, transport, routing, data link protocols, local area networks, network management, and network security. Students cannot receive credit for both CSCI 4431 / CSCI 4431W taken while an undergraduate and CSCI 6431 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4431 / CSCI 4431W and CSCI 6431 . Prerequisite: CSCI 6461 . (Fall, Every year)

CSCI 6433. Internet Protocols. 3 Credits.

Understanding of the layered protocols for the Internet. Interconnection of networks. The IP protocol and routing algorithms, switches, bridges, and routers. The transmission control protocol (TCP). Addressing and names. Application-specific protocols, FTP, TELNET, SMTP, SNMP, HTTP. Domain name services. Prerequisites: CSCI 6221 and CSCI 6431 . (Fall and spring, Every year)

CSCI 6434. Design of Internet Protocols. 3 Credits.

Protocol specifications and formal description methods. Finite-state descriptions of Internet protocols. Specification and Description Language. Implementation of protocol specifications. Prerequisites: CSCI 6212 and CSCI 6433 . (Fall and spring, Every year)

CSCI 6441. Database Management Systems. 3 Credits.

Design and architecture of relational database management systems; query languages, data models, index structures, database application design. Students cannot receive credit for CSCI 2441W or 2541W taken while an undergraduate and CSCI 6441 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for CSCI 2441W or CSCI 2541W and CSCI 6441 . Prerequisites: CSCI 6221 and CSCI 6461 . (Spring, Every year)

CSCI 6442. Database Systems II. 3 Credits.

Concepts in database systems. Relational database design. Editing, report generation, updating, schema refinement, tuning. Construction of database management systems. Conceptual and logical design of a database. Prerequisite: CSCI 6441 .

CSCI 6443. Data Mining. 3 Credits.

Fundamental concepts of data mining. Algorithm techniques for data mining, including classification, clustering, association rules mining. Prerequisites: CSCI 6441 or permission of the instructor. (Fall and spring, Every year)

CSCI 6444. Introduction to Big Data and Analytics. 3 Credits.

Big data, its properties, technology and the types and classes of analytics that can be applied to it; associated storage and programming systems. Students gain practical experience through focused projects to apply different analytics to a data set. Prerequisites: CSCI 2113 , CSCI 6221 , or EMSE 6574 . Recommended background: Experience using programming tools SPSS, STATA, R, or SAS is advantageous but not required. (Fall, spring, and summer, Every year)

CSCI 6451. Information Retrieval Systems. 3 Credits.

Information organization and retrieval of natural language data by digital computer systems; statistical, syntactic, and logical analysis of natural language; dictionary and thesaurus systems; searching strategies and cataloging. Large-scale file structures. Prerequisites: CSCI 6221 and CSCI 6461 . (Fall and spring, Every year)

CSCI 6461. Computer System Architecture. 3 Credits.

Concepts in processor, system, and network architectures; architecture of pipeline, superscalar, and VLIW/EPIC processors; multiprocessors and interconnection networks; cache coherence and memory subsystem design for multiprocessor architectures; parallel and distributed system architecture; internetworking. Restricted to graduate students. (Fall, spring, and summer, Every year)

CSCI 6511. Artificial Intelligence. 3 Credits.

Representation and space search; heuristic search; predicate calculus; knowledge representation and knowledge engineering for expert systems; rule-based, hybrid, and O-O systems; semantic nets, frames, and natural language; theorem provers; planning, learning, neural nets; use of AI languages. Prerequisite: CSCI 6212 . (Spring, Every year)

CSCI 6515. Natural Language Understanding. 3 Credits.

The state of the art of natural language parsing and semantic understanding by computer systems. Review of formal, context-free, and transformational grammars and parsing. Augmented transition networks: problems of complexity, semantics, and context. Deterministic parsing and semantic parsing. Prerequisite: CSCI 6511 .

CSCI 6521. Introduction to Mobile Robotics. 3 Credits.

Concepts of autonomous mobile robotics with emphasis on algorithmic aspects. Sensing, sensor fusion, localization, calibration, mapping, perception, decision making, planning, behavior-based control, world modeling, and navigation. Development of algorithms that can operate autonomous mobile platforms in complex, real-world environments. Prerequisites: MATH 1232 and MATH 2184 ; and CSCI 6362 or CSCI 4341 . (Fall and spring, Every year)

CSCI 6525. Autonomous Robotics: Manipulation. 3 Credits.

Manipulation and autonomous operation in dynamic, human-centric environments. Reinforcement learning, perception, optimization algorithms, kinematics, dynamics, control theory. Prerequisites: CSCI 6362 and MATH 2184 ; or permission of the instructor. (Fall and spring, Every year)

CSCI 6527. Introduction to Computer Vision. 3 Credits.

Image signal processing and filtering. Saliency, features and feature extraction, tracking, stereo disparity estimation, structure form motion, photogrammetry, optic flow, homography estimation and warping, scene segmentation, place recognition, object recognition, robust estimation, and camera calibration. Current research topics. Prerequisites: MATH 1232 and MATH 2184 ; and CSCI 6362 or CSCI 6341 . (Fall and spring, Every year)

CSCI 6531. Computer Security. 3 Credits.

Functional description of cryptographic primitives; risk analysis; policy models; design principles; assurance; malicious logic. Students cannot receive credit for both CSCI 4531 taken while an undergraduate and CSCI 6531 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4531 and CSCI 6531 . Prerequisite: CSCI 6461 . (Spring, Every year)

CSCI 6532. Information Policy. 3 Credits.

CSCI 6533. Intro to Usable Security & Privacy. 3 Credits.

Introduction to concepts and research methods in human factors in computer security and privacy; reading, writing, reviewing scientific literature; developing research problems for exploration and presentation. Restricted to computer science program. Corequisites: CSCI 6221 , or CSCI 6461 , or CSCI 6212 . Credit cannot be earned for this course and CSCI 4533 . (Spring, Every year)

CSCI 6534. Information Security in Government. 1-3 Credits.

Roles, issues, and governance of cyber security in the federal government. Overview of the technical aspects of cyber security including federally developed cybersecurity standards and frameworks and government and industry initiatives. Prerequisites: Permission of the instructor. (Spring and fall, Every year)

CSCI 6541. Network Security. 3 Credits.

Security protocols and applications in local, global, and wireless networks; IPSec and packet-level communication security systems; network authentication and key-exchange protocols; intrusion detection systems and firewalls; secure network applications; network worms and denial-of-service attacks. Students cannot receive credit for both CSCI 4541 taken while an undergraduate and CSCI 6541 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4541 and CSCI 6541 . Prerequisite: CSCI 6531 . (Spring, Every year)

CSCI 6542. Computer Network Defense. 3 Credits.

Offensive and defensive information warfare operations. Simulation of various attacks on and defenses of computer systems. Laws related to information warfare. History and literature related to information warfare attacks. Prerequisite: CSCI 6541 .

CSCI 6545. Software Security. 3 Credits.

Security for software systems. Theory and practice of designing and implementing secure software. Security in the context of software engineering. Practical experience with building a software system and securing it, with emphasis on correctness and robustness. Requires substantial prior programming experience. Prerequisites: CSCI 6461 or CSCI 6411 ; and CSCI 6531 or EMSE 6540 ; or permission of the instructor. (Fall and spring, Every year)

CSCI 6547. Wireless and Mobile Security. 3 Credits.

Mobile agents, wireless Web, WAP, WEP, peer-to-peer computing; secure routing; intrusion detection and authentication on wireless networks; security for handheld devices; encryption and cryptographic measures for wireless; real-time wireless security; security measures for embedded devices. Prerequisites: CSCI 6431 and CSCI 6531 . (Spring, Every year)

CSCI 6548. E-Commerce Security. 3 Credits.

Advanced technical topics in e-commerce security. Key security threats. Authentication and authorization models, directory services, cloud based IAM, federated identity. Public key cryptography and PKI. Mobile payment methods, digital currencies, blockchain. Technologies and applications for securing web commerce. Web service security standards. Prerequisites: CS 4531 or CS 6531. (Fall and spring, Every year)

CSCI 6554. Computer Graphics II. 3 Credits.

Algorithmic aspects of computer graphics; 3D viewing transformation; shape modeling; shading and illumination models; visible-surface determination; curves and surfaces; sampling and aliasing; global illumination, ray tracing and radiosity; shadows; image manipulation and texture mapping; procedural models. (Spring, Every year)

CSCI 6555. Computer Animation. 3 Credits.

Euler angles and quaternions; articulated figure motion; forward and inverse kinematics; kinematic, physics based, and behavioral motion control; character animation; motion capture; temporal aliasing; sound synthesis and synchronization. (Fall, Every year)

CSCI 6561. Design of Human–Computer Interface. 3 Credits.

Design of dialogues for interactive systems. Psychological, physiological, linguistic, and perceptual factors. Advantages and disadvantages of various interaction techniques, command language syntaxes, and data presentations. Design methodology and guidelines. Case studies, research readings, and projects. Prerequisite: CSCI 6221 .

CSCI 6562. Design of Interactive Multimedia. 3 Credits.

History, theory, and development of multimedia concepts. Hardware components, platforms, and authoring tools. Scientific, technical, and cognitive foundations of various media including text, sound, graphics, and video. Interface design. Use of a media taxonomy as a design and evaluation tool. Completion of a multimedia portfolio required. Prerequisite: CSCI 6221 .

CSCI 6572. Computational Biology Algorithms. 3 Credits.

Algorithms and models for DNA and protein sequence alignments, gene finding, identification of gene regulatory regions, sequence evolution and phylogenetics, RNA and protein structure, microarray and/or proteomics data analysis. Prerequisites: CSCI 6212 ; and programming experience in C/C++ or Java. (Spring, Every year)

CSCI 6900. Colloquium. 0 Credits.

Lectures by outstanding authorities in computer science. PhD students in computer science must take the course for a number of different lecture topics determined by the department. Consult the Schedule of Classes for additional information.

CSCI 6907. Special Topics. 3 Credits.

Topics vary by semester. May be repeated for credit if the topic differs. See department for details. (Fall and spring, Every year)

CSCI 6908. Research. 1-12 Credits.

Applied research and experimentation projects, as arranged. May be repeated for credit.

CSCI 6917. Guided Research for Graduate Students I. 3 Credits.

Practice research methods, including validation of hypothesis, synthesis and problem solving, and validation methods. Synthesize scientific output including experimental results and theoretical analysis. Present outcomes. Prerequisites: CSCI 6212 and CSCI 6221 .

CSCI 6918. Guided Research for Graduate Students II. 3 Credits.

Adds depth to CSCI 6917 . Practice research methods, including validation of hypothesis, synthesis and problem solving, and validation methods. Synthesize scientific output including experimental results and theoretical analysis. Present outcomes. Prerequisites: CSCI 6212 and CSCI 6221 . Corequisites: CSCI 6917 .

CSCI 6998. Thesis Research. 3 Credits.

CSCI 6999. Thesis Research. 3 Credits.

CSCI 8211. Advanced Topics in Algorithms. 3 Credits.

Graph algorithms, strongly connected components, biconnected components, dominators in acyclic graphs, ordered trees, network flow, planarity testing, bipartite matching, theory of NP completeness, NP-complete problems. Design and analysis of approximation algorithms for NP-complete problems. Prerequisite: CSCI 6212 .

CSCI 8231. Advanced Topics in Software Engineering. 3 Credits.

Seminar on current research and developments in software engineering. Students develop a software package with the aid of available software tools such as requirement tool, design tool, code generators, testing tools, measurement tools, cost estimation tools. Prerequisites: CSCI 6232 and CSCI 6233 . (Fall and spring, Every year)

CSCI 8331. Advanced Cryptography. 3 Credits.

Introduction to secure multi-party computation (MPC) and related topics. Basic definitions and core MPC protocols and reading and presenting the latest research on the topic. Includes practical experience with MPC implementation. Prerequisites: One of the following: CSCI 2312 , CSCI 3212 , CSCI 3313 , CSCI 6212 , MATH 2971 , or MATH 2971W . Recommended background: Some experience with algorithms and cryptography. (Spring, Every year)

CSCI 8401. Advanced Topics in Systems. 3 Credits.

Seminar on current research and developments in computer operating systems. May be repeated for credit. (Spring, Even years)

CSCI 8431. Advanced Topics in Computer Networks and Networked Computing. 3 Credits.

Seminar on current research and developments in computer networks, Internet, networked computing, mobile computing and pervasive computing. May be repeated for credit. Prerequisites: CSCI 6461 , CSCI 6212 and CSCI 6433 . (Fall and spring, Every year)

CSCI 8440. Advanced Topics in Data Management. 3 Credits.

Seminar on current research and developments in computer database systems and information retrieval. May be repeated for credit. Prerequisite: CSCI 6442 or CSCI 6451 .

CSCI 8531. Advanced Topics in Security. 3 Credits.

Seminar on current research and developments in information assurance. May be repeated for credit. Prerequisite: CSCI 6531 .

CSCI 8554. Advanced Topics in Computer Graphics. 3 Credits.

Seminar on current research and developments in computer graphics. Spatial and temporal anti-aliasing: hidden-surface algorithms: illumination models, radiosity, textural mapping. May be repeated for credit. Prerequisite: CSCI 6554 .

CSCI 8900. Advanced Selected Topics. 3 Credits.

Topics announced in the Schedule of Classes.

CSCI 8901. Research and Evaluation Methods. 3 Credits.

Required for all computer science doctoral candidates. The scientific method; research/design requirements and objectives: qualitative, quantitative, and case studies; performance metrics; design procedures and control; sources of error and bias; evaluation tools; formal validation methods; documentation standards. Prerequisite: APSC 3115 .

CSCI 8998. Computer Science Research. 1-12 Credits.

May be repeated for credit. Restricted to doctoral candidates preparing for the qualifying examination. (Fall and spring, Every year)

CSCI 8999. Dissertation Research. 1-12 Credits.

Doctoral candidates performing dissertation research. Restricted to doctoral candidates. (Fall and spring, Every year)

Print Options

Send Page to Printer

Print this page.

Download Page (PDF)

The PDF will include all information unique to this page.

Download PDF of the 2023-2024 Bulletin

All pages in the 2023-2024 Bulletin.

Problem Solving

Nidal's blog

Photo by Tamara Gak on Unsplash

Hybrid Approach to Problem Solving: The Knowledge-Based and Exploratory Methods

Introduction to problem-solving techniques.

Table of contents

The knowledge-based approach, the exploratory approach, the hybrid approach, key takeaways.

In the ever-evolving world of software development, problem-solving is a crucial skill. It is not just about finding the right solution, but also about understanding the problem and its context. In this article, we will explore two distinct methods of problem-solving that I've used before and suggest a hybrid approach to them.

The knowledge-based approach to problem-solving is based on crystalized memory. This approach is generally faster as it relies on pre-existing knowledge and solutions. It is like having a well-stocked library at your fingertips, ready to provide you with the answer to common problems.

However, this approach might not be suitable for new problems that aren't part of the background knowledge or are a bit different, making them difficult to recognize. It is like trying to find a specific book in a library that has never been built. You might need to build that library yourself, which is time-consuming and requires a lot of effort.

I am trying to illustrate in the image above the patterns that we just see and recognize, hopefully it won't be too abstract for readers. The geometric shapes in the image represent the patterns of knowledge and problem-solving.

The exploratory approach to problem-solving, on the other hand, involves turning chaos into order 🦞 by exploring the outcomes and understanding the heuristics. This approach is slower and might require some luck, but it provides valuable real-deal experience. It is like exploring a new city without a map, which can be frustrating and confusing, but also exciting and enlightening.

The exploratory approach is more about understanding the problem and its context, rather than just finding the right solution. It is about learning from the process, understanding the outcomes, documenting, and applying the heuristics to future problems.

I am trying to illustrate in the image above simple shapes on the bottom-left, which represent basic knowledge and solutions, to slightly more complex shapes in the middle to the top-right of the image, which represent advanced knowledge and problem-solving techniques. This progression mirrors the learning process, where we start with simple patterns and gradually develop more complex ones as we gain more knowledge and experience.

The hybrid approach to problem-solving begins with the exploratory method, which is especially useful when you have a significant amount of time and the problem at hand is of high importance. In this phase, you solve the problem yourself, diving deep into the problem, understanding its semantics, and breaking it down into smaller, more manageable pieces. This approach is like having a well-stocked library and also exploring a new city without a map.

The exploratory phase is crucial when you can afford it in terms of time. It's akin to conducting a thorough investigation, gathering as much information as possible, and understanding the problem in its entirety. This phase involves research, trial-and-error, asking questions, and testing various hypotheses. It's during this phase that you might interact with AI solutions like ChatGPT to explore potential solutions and understand the design and semantics behind them.

Once you have a solution, you can then compare it with the best solution out there. This is the knowledge-based approach. It's like having a well-stocked library and reading the best books on the topic. You're not just learning about the problem, but also understanding the reasons behind the solution or design. This phase is crucial as it helps you understand the problems that were faced and the solutions that have been provided.

This hybrid approach combines the speed of the knowledge-based approach with the depth of understanding of the exploratory approach. It allows for a more comprehensive understanding of the problem and its solution.

Problem-solving is a crucial skill in software development, requiring an understanding of the problem and its context.

The knowledge-based approach to problem-solving relies on pre-existing knowledge and solutions, making it generally faster but less suitable for new problems.

The exploratory approach involves understanding the problem and its context and learning from the process. It is slower and might require some luck, but provides valuable experience whatever the outcome.

The hybrid approach to problem-solving combines the knowledge-based and exploratory approaches. It begins with the exploratory method, which is especially useful when you have a significant amount of time and the problem at hand is of high importance. After finding a solution, you can then compare it with the best solution out there, which is the knowledge-based approach. This hybrid approach combines the speed of the knowledge-based approach with the depth of understanding of the exploratory approach, allowing for a more comprehensive understanding of the problem and its solution.

The hybrid approach to problem-solving is a powerful tool that combines the exploratory and knowledge-based methods. It allows you to solve problems comprehensively and efficiently, leveraging the best of both worlds. Whether you're dealing with a complex programming task or a personal challenge, this approach can help you find the best solution.

https://youtu.be/UfcAVejslrU 🎶 MUSIC BIT: Problem-solving in software development is not merely about finding solutions, but also about understanding the problem and its context. This exploration is enriched when we incorporate elements from the realm of music, such as the song 'Weightless' by Marconi Union. 'Weightless' is a renowned instrumental track known for its calming effect. It features dreamy synths, soothing melodies, and instrumentation that includes piano, guitar, and electronic samples of natural soundscapes. Scientific research has validated its effectiveness, indicating that it can slow heart rate, reduce blood pressure, and decrease cortisol levels, a hormone released in response to stress . The calming nature of 'Weightless' can be likened to the peace and relaxation we experience when we solve a complex problem. Our journey of problem-solving is filled with ups and downs, but ultimately, we find satisfaction when we find the right solution. In our next meeting in two weeks, keep exploring, keep learning, and find your 'Weightless' moments. Goodbye for now!

Did you find this article valuable?

Support Nidal Iguer by becoming a sponsor. Any amount is appreciated!

What is Programming? A Handbook for Beginners

Welcome to the amazing world of programming. This is one of the most useful and powerful skills that you can learn and use to make your visions come true.

In this handbook, we will dive into why programming is important, its applications, its basic concepts, and the skills you need to become a successful programmer.

You will learn:

• What programming is and why it is important .
• What a programming language is and why it is important .
• How programming is related to binary numbers .
• Real-world applications of programming .
• Skills you need to succeed as a programmer .
• Tips for learning how to code .
• Basic programming concepts .
• Types of programming languages .
• How to contribute to open source projects .
• And more...

Are you ready? Let's begin! ✨  

🔹 What is Programming?

Did you know that computer programming is already a fundamental part of your everyday lives? Let's see why. I'm sure that you will be greatly surprised.

Every time you turn on your smartphone, laptop, tablet, smart TV, or any other electronic device, you are running code that was planned, developed, and written by developers. This code creates the final and interactive result that you can see on your screen.

That is exactly what programming is all about. It is the process of writing code to solve a particular problem or to implement a particular task.

Programming is what allows your computer to run the programs you use every day and your smartphone to run the apps that you love. It is an essential part of our world as we know it.

Whenever you check your calendar, attend virtual conferences, browse the web, or edit a document, you are using code that has been written by developers.

"And what is code?" you may ask.

Code is a sequence of instructions that a programmer writes to tell a device (like a computer) what to do.

The device cannot know by itself how to handle a particular situation or how to perform a task. So developers are in charge of analyzing the situation and writing explicit instructions to implement what is needed.

To do this, they follow a particular syntax (a set of rules for writing the code).

A developer (or programmer) is the person who analyzes a problem and implements a solution in code.

Sounds amazing, right? It's very powerful and you can be part this wonderful world too by learning how to code. Let's see how.

You, as a developer.

Let's put you in a developer's shoes for a moment. Imagine that you are developing a mobile app, like the ones that you probably have installed on your smartphone right now.

What is the first thing that you would do?

Think about this for a moment.

The answer is...

Analyzing the problem. What are you trying to build?

As a developer, you would start by designing the layout of the app, how it will work, its different screens and functionality, and all the small details that will make your app an awesome tool for users around the world.

Only after you have everything carefully planned out, you can start to write your code. To do that, you will need to choose a programming language to work with. Let's see what a programming language is and why they are super important.

🔸 What is a Programing Language?

A programming language is a language that computers can understand.

We cannot just write English words in our program like this:

"Computer, solve this task!"

and hope that our computer can understand what we mean. We need to follow certain rules to write the instructions.

Every programming language has its own set of rules that determine if a line of code is valid or not. Because of this, the code you write in one programming language will be slightly different from others.

💡 Tip: Some programming languages are more complex than others but most of them share core concepts and functionality. If you learn how to code in one programming language, you will likely be able to learn another one faster.

Before you can start writing awesome programs and apps, you need to learn the basic rules of the programming language you chose for the task.

💡 Tip: a program is a set of instructions written in a programming language for the computer to execute. We usually write the code for our program in one or multiple files.

For example, this is a line of code in Python (a very popular programming language) that shows the message "Hello, World!" :

But if we write the same line of code in JavaScript (a programming language mainly used for web development), we will get an error because it will not be valid.

To do something very similar in JavaScript, we would write this line of code instead:

Visually, they look very different, right? This is because Python and JavaScript have a different syntax and a different set of built-in functions .

💡 Tip : built-in functions are basically tasks that are already defined in the programming language. This lets us use them directly in our code by writing their names and by specifying the values they need.  

In our examples, print() is a built-in function in Python while console.log() is a function that we can use in JavaScript to see the message in the console (an interactive tool) if we run our code in the browser.

Examples of programming languages include Python, JavaScript, TypeScript, Java, C, C#, C++, PHP, Go, Swift, SQL, and R. There are many programming languages and most of them can be used for many different purposes.

💡 Tip: These were the most popular programming languages on the Stack Overflow Developer Survey 2022 :

There are many other programming languages (hundreds or even thousands!) but usually, you will learn and work with some of the most popular ones. Some of them have broader applications like Python and JavaScript while others (like R) have more specific (and even scientific) purposes.

This sounds very interesting, right? And we are only starting to talk about programming languages. There is a lot to learn about them and I promise you that if you dive deeper into programming, your time and effort will be totally worth it.

Awesome! Now that you know what programming is and what programming languages are all about, let's see how programming is related to binary numbers.

🔹 Programming and Binary Numbers

When you think about programming, perhaps the first thing that comes to your mind is something like the below image, right? A sequence of 0 s and 1 s on your computer.

Programming is indeed related to binary numbers ( 0 and 1 ) but in an indirect way. Developers do not actually write their code using zeros and ones.

We usually write programs in a high-level programming language, a programming language with a syntax that recognizes specific words (called keywords), symbols, and values of different data types.

Basically, we write code in a way that humans can understand.

For example, these are the keywords that we can use in Python:

Every programming language has its own set of keywords (words written in English). These keywords are part of the syntax and core functionality of the programming language.

But keywords are just common words in English, almost like the ones that we would find in a book.

That leads us to two very important questions:

• How does the computer understand and interpret what we are trying to say?
• Where does the binary number system come into play here?

The computer does not understand these words, symbols, or values directly.

When a program runs, the code that we write in a high-level programming language that humans can understand is automatically transformed into binary code that the computer can understand.

This transformation of source code that humans can understand into binary code that the computer can understand is called compilation .

According to Britannica , a compiler is defined as:

Computer software that translates (compiles) source code written in a high-level language (e.g., C++) into a set of machine-language instructions that can be understood by a digital computer’s CPU.

Britannica also mentions that:

The term compiler was coined by American computer scientist Grace Hopper , who designed one of the first compilers in the early 1950s.

Some programming languages can be classified as compiled programming languages while others can be classified as interpreted programming languages based on how to they are transformed into machine-language instructions.

However, they all have to go through a process that converts them into instructions that the computer can understand.

Awesome. Now you know why binary code is so important for computer science. Without it, basically programming would not exist because computers would not be able to understand our instructions.

Now let's dive into the applications of programming and the different areas that you can explore.

🔸 Real-World Applications of Programming

Programming has many different applications in many different industries. This is truly amazing because you can apply your knowledge in virtually any industry that you are interested in.

From engineering to farming, from game development to physics, the possibilities are endless if you learn how to code.  

Let's see some of them. (I promise you. They are amazing! ⭐) .

Front-End Web Development

If you learn how to code, you can use your programming skills to design and develop websites and online platforms. Front-End Web Developers create the parts of the websites that users can see and interact with directly.

For example, right now you are reading an article on freeCodeCamp 's publication. The publication looks like this and it works like this thanks to code that front-end web developers wrote line by line.

💡 Tip: If you learn front-end web development, you can do this too.

Front-End Web Developers use HTML and CSS to create the structure of the website (these are markup languages, which are used to present information) and they write JavaScript code to add functionality and interactivity.

If you are interested in learning front-end web development, you can learn HTML and CSS with these free courses on freeCodeCamp's YouTube Channel:

• Learn HTML5 and CSS3 From Scratch - Full Course
• Learn HTML & CSS – Full Course for Beginners
• Frontend Web Development Bootcamp Course (JavaScript, HTML, CSS)
• Introduction To Responsive Web Design - HTML & CSS Tutorial

You can also learn JavaScript for free with these free online courses:

• Learn JavaScript - Full Course for Beginners
• JavaScript Programming - Full Course
• JavaScript DOM Manipulation – Full Course for Beginners
• Learn JavaScript by Building 7 Games - Full Course

💡 Tip: You can also earn a Responsive Web Design Certification while you learn with interactive exercises on freeCodeCamp.

Back-End Web Development

More complex and dynamic web applications that work with user data also require a server . This is a computer program that receives requests and sends appropriate responses. They also need a database , a collection of values stored in a structured way.

Back-End Web Developers are in charge of developing the code for these servers. They decide how to handle the different requests, how to send appropriate resources, how to store the information, and basically how to make everything that runs behind the scenes work smoothly and efficiently.

A real-world example of back-end web development is what happens when you create an account on freeCodeCamp and complete a challenge. Your information is stored on a database and you can access it later when you sign in with your email and password.

This amazing interactive functionality was implemented by back-end web developers.

💡 Tip: Full-stack Web Developers are in charge of both Front-End and Back-End Web Development. They have specialized knowledge on both areas.

All the complex platforms that you use every day, like social media platforms, online shopping platforms, and educational platforms, use servers and back-end web development to power their amazing functionality.

Python is an example of a powerful programming language used for this purpose. This is one of the most popular programming languages out there, and its popularity continues to rise every year. This is partly because it is simple and easy to learn and yet powerful and versatile enough to be used in real-world applications.

💡 Tip: if you are curious about the specific applications of Python, this is an article I wrote on this topic .

JavaScript can also be used for back-end web development thanks to Node.js.

Other programming languages used to develop web servers are PHP, Ruby, C#, and Java.

If you would like to learn Back-End Web Development, these are free courses on freeCodeCamp's YouTube channel:

• Python Backend Web Development Course (with Django)
• Node.js and Express.js - Full Course
• Full Stack Web Development for Beginners (Full Course on HTML, CSS, JavaScript, Node.js, MongoDB)
• Node.js / Express Course - Build 4 Projects

💡 Tip: freeCodeCamp also has a free Back End Development and APIs certification.

Mobile App Development

Mobile apps have become part of our everyday lives. I'm sure that you could not imagine life without them.

Think about your favorite mobile app. What do you love about it?

Our favorite apps help us with our daily tasks, they entertain us, they solve a problem, and they help us to achieve our goals. They are always there for us.

That is the power of mobile apps and you can be part of this amazing world too if you learn mobile app development.

Developers focused on mobile app development are in charge of planning, designing, and developing the user interface and functionality of these apps. They identify a gap in the existing apps and they try to create a working product to make people's lives better.

💡 Tip: regardless of the field you choose, your goal as a developer should always be making people's lives better. Apps are not just apps, they have the potential to change our lives. You should always remember this when you are planning your projects. Your code can make someone's life better and that is a very important responsibility.

Mobile app developers use programming languages like JavaScript, Java, Swift, Kotlin, and Dart. Frameworks like Flutter and React Native are super helpful to build cross-platform mobile apps (that is, apps that run smoothly on multiple different operating systems like Android and iOS).

According to Flutter 's official documentation:

Flutter is an open source framework by Google for building beautiful, natively compiled, multi-platform applications from a single codebase.

If you would like to learn mobile app development, these are free courses that you can take on freeCodeCamp's YouTube channel:

• Flutter Course for Beginners – 37-hour Cross Platform App Development Tutorial
• Flutter Course - Full Tutorial for Beginners (Build iOS and Android Apps)
• React Native - Intro Course for Beginners
• Learn React Native Gestures and Animations - Tutorial

Game Development

Games create long-lasting memories. I'm sure that you still remember your favorite games and why you love (or loved) them so much. Being a game developer means having the opportunity of bringing joy and entertainment to players around the world.

Game developers envision, design, plan, and implement the functionality of a game. They also need to find or create assets such as characters, obstacles, backgrounds, music, sound effects, and more.

💡 Tip: if you learn how to code, you can create your own games. Imagine creating an awesome and engaging game that users around the world will love. That is what I personally love about programming. You only need your computer, your knowledge, and some basic tools to create something amazing.

Popular programming languages used for game development include JavaScript, C++, Python, and C#.

If you are interested in learning game development, you can take these free courses on freeCodeCamp's YouTube channel:

• JavaScript Game Development Course for Beginners
• Learn Unity - Beginner's Game Development Tutorial
• Learn Python by Building Five Games - Full Course
• Code a 2D Game Using JavaScript, HTML, and CSS (w/ Free Game Assets) – Tutorial
• 2D Game Development with GDevelop - Crash Course
• Pokémon Coding Tutorial - CS50's Intro to Game Development

Biology, Physics, and Chemistry

Programming can be applied in every scientific field that you can imagine, including biology, physics, chemistry, and even astronomy. Yes! Scientists use programming all the time to collect and analyze data. They can even run simulations to test hypotheses.

In biology, computer programs can simulate population genetics and population dynamics. There is even an entire field called bioinformatics .

According to this article "Bioinformatics" by Ardeshir Bayat, member of the Centre for Integrated Genomic Medical Research at the University of Manchester:

Bioinformatics is defined as the application of tools of computation and analysis to the capture and interpretation of biological data.

Dr. Bayat mentions that bioinformatics can be used for genome sequencing. He also mentions that its discoveries may lead to drug discoveries and individualized therapies.

Frequently used programming languages for bioinformatics include Python, R, PHP, PERL, and Java.

💡 Tip: R is a programming "language and environment for statistical computing and graphics" ( source ).

An example of a great tool that scientists can use for biology is Biopython . This is a Python framework with "freely available tools for biological computation."

If you would like to learn more about how you can apply your programming skills in science, these are free courses that you can take on freeCodeCamp's YouTube channel:

• Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
• R Programming Tutorial - Learn the Basics of Statistical Computing
• Learn Python - Full Course for Beginners [Tutorial]

Physics requires running many simulations and programming is perfect for doing exactly that. With programming, scientists can program and run simulations based on specific scenarios that would be hard to replicate in real life. This is much more efficient.

Programming languages that are commonly used for physics simulations include C, Java, Python, MATLAB, and JavaScript.  

Chemistry also relies on simulations and data analysis, so it's a field where programming can be a very helpful tool.

In this scientific article by Dr. Ivar Ugi and his colleagues from Organisch-chemisches Institut der Technischen Universität München, they mention that:

The design of entirely new syntheses, and the classification and documentation of structures, substructures, and reactons are examples of new applications of computers to chemistry.

Scientific experiments also generate detailed data and results that can be analyzed with computer programs developed by scientists.  

Think about it: writing a program to generate a box plot or a scatter plot or any other type of plot to visualize trends in thousands of measurements can save researchers a lot of time and effort. This lets them focus on the most important part of their work: analyzing the results.

💡 Tips: if you are interested in diving deeper into this, this is a list of chemistry simulations by the American Chemical Society. These simulations were programmed by developers and they are helping thousands of students and teachers around the world.

Think about it...You could build the next great simulation. If you are interested in a scientific field, I totally recommend learning how to code. Your work will be much more productive and your results will be easier to analyze.

If you are interested in learning programming for scientific applications, these are free courses on freeCodeCamp's YouTube channel:

• Python for Data Science - Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib)

Data Science and Engineering

Talking about data...programming is also essential for a field called Data Science . If you are interested in answering questions through data and statistics, this field might be exactly what you are looking for and having programming skills will help you to achieve your goals.

Data scientists collect and analyze data in order to answer questions in many different fields. According to UC Berkeley in the article " What is Data Science? ":

Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions.

There are many powerful programming languages for analyzing and visualizing data, but perhaps one of the most frequently used ones for this purpose is Python.

This is an example of the type of data visualizations that you can create with Python. They are very helpful to analyze data visually and you can customize them to your fit needs.

If you are interested in learning programming for data science, these are free courses on freeCodeCamp's YouTube channel:

• Learn Data Science Tutorial - Full Course for Beginners
• Intro to Data Science - Crash Course for Beginners
• Build 12 Data Science Apps with Python and Streamlit - Full Course
• Data Analysis with Python - Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn)

💡 Tip: you can also earn these free certifications on freeCodeCamp:

• Data Visualization
• Data Analysis with Python

Engineering

Engineering is another field where programming can help you to succeed. Being able to write your own computer programs can make your work much more efficient.

There are many tools created specifically for engineers. For example, the R programming language is specialized in statistical applications and Python is very popular in this field too.

Another great tool for programming in engineering is MATLAB . According to its official website:

MATLAB is a programming and numeric computing platform used by millions of engineers and scientists to analyze data, develop algorithms, and create models.

Really, the possibilities are endless.

You can learn MATLAB with this crash course on the freeCodeCamp YouTube channel .

If you are interested in learning engineering tools related to programming, this is a free course on freeCodeCamp's YouTube channel that covers AutoCAD, a 2D and 3D computer-aided design software used by engineers:

• AutoCAD for Beginners - Full University Course

Medicine and Pharmacology

Medicine and pharmacology are constantly evolving by finding new treatments and procedures. Let's see how you can apply your programming skills in these fields.

Programming is really everywhere. If you are interested in the field of medicine, learning how to code can be very helpful for you too. Even if you would like to focus on computer science and software development, you can apply your knowledge in both fields.

Specialized developers are in charge of developing and writing the code that powers and controls the devices and machines that are used by modern medicine.

Think about it...all these machines and devices are controlled by software and someone has to write that software. Medical records are also stored and tracked by specialized systems created by developers. That could be you if you decide to follow this path. Sounds exciting, right?

According to the scientific article Application of Computer Techniques in Medicine :

Major uses of computers in medicine include hospital information system, data analysis in medicine, medical imaging laboratory computing, computer assisted medical decision making, care of critically ill patients, computer assisted therapy and so on.

Pharmacology

Programming and computer science can also be applied to develop new drugs in the field of pharmacology.

A remarkable example of what you can achieve in this field by learning how to code is presented in this article by MIT News. It describes how an MIT senior, Kristy Carpenter, was using computer science in 2019 to develop "new, more affordable drugs." Kristy mentions that:

Artificial intelligence, which can help compute the combinations of compounds that would be better for a particular drug, can reduce trial-and-error time and ideally quicken the process of designing new medicines.

Another example of a real-world application of programming in pharmacology is related to Python (yes, Python has many applications!). Among its success stories , we find that Python was selected by AstraZeneca to develop techniques and programs that can help scientists to discover new drugs faster and more efficiently.

The documentation explains that:

To save time and money on laboratory work, experimental chemists use computational models to narrow the field of good drug candidates, while also verifying that the candidates to be tested are not simple variations of each other's basic chemical structure.

If you are interested in learning programming for medicine or health-related fields, this is a free course on freeCodeCamp's YouTube channel on programming for healthcare imaging:

• PyTorch and Monai for AI Healthcare Imaging - Python Machine Learning Course

Have you ever thought that programming could be helpful for education? Well, let me tell you that it is and it is very important. Why? Because the digital learning tools that students and teachers use nowadays are programmed by developers.

Every time a student opens an educational app, browses an educational platform like freeCodeCamp, writes on a digital whiteboard, or attends a class through an online meeting platform, programming is making that possible.

As a programmer or as a teacher who knows how to code, you can create the next great app that will enhance the learning experience of students around the world.

Perhaps it will be a note-taking app, an online learning platform, a presentation app, an educational game, or any other app that could be helpful for students.

The important thing is to create it with students in mind if your goal is to make something amazing that will create long-lasting memories.

If you envision it, then you can create it with code.  

Teachers can also teach their students how to code to develop their problem-solving skills and to teach them important skills for their future.

💡 Tip: if you are teaching students how to code, Scratch is a great programming language to teach the basics of programming. It is particularly focused on teaching children how to code in an interactive way.

According to the official Scratch website:

Scratch is the world’s largest coding community for children and a coding language with a simple visual interface that allows young people to create digital stories, games, and animations.

If you are interested in learning how to code for educational purposes, these are courses that you may find helpful on freeCodeCamp's YouTube channel:

• Scratch Tutorial for Beginners - Make a Flappy Bird Game
• Computational Thinking & Scratch - Intro to Computer Science - Harvard's CS50 (2018)
• Android Development for Beginners - Full Course

Machine Learning, Artificial Intelligence, and Robotics

Some of the most amazing fields that are directly related to programming are Machine Learning, Artificial Intelligence, and Robotics. Let's see why.

Artificial Intelligence is defined by Britannica as:

The project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.

Machine learning is a branch or a subset of the field of Artificial Intelligence in which systems can learn on their own based on data. The goal of this learning process is to predict the expected output. These models continuously learn how to "think" and how to analyze situations based on their previous training.

The most commonly used programming languages in these fields are Python, C, C#, C++, and MATLAB.

Artificial intelligence and Machine Learning have amazing applications in various industries, such as:

• Image and object detection.
• Making predictions based on patterns.
• Text recognition.
• Recommendation engines (like when an online shopping platform shows you products that you may like or when YouTube shows you videos that you may like).
• Spam detection for emails.
• Fraud detection.
• Social media features like personalized feeds.
• Many more... there are literally millions of applications in virtually every industry.

If you are interested in learning how to code for Artificial Intelligence and Machine Learning, these are free courses on freeCodeCamp's YouTube channel:

• Machine Learning for Everybody – Full Course
• Machine Learning Course for Beginners
• PyTorch for Deep Learning & Machine Learning – Full Course
• TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
• Self-Driving Car with JavaScript Course – Neural Networks and Machine Learning
• Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial
• Practical Deep Learning for Coders - Full Course from fast.ai and Jeremy Howard
• Deep Learning Crash Course for Beginners
• Advanced Computer Vision with Python - Full Course

💡 Tip: you can also earn a Machine Learning with Python Certification on freeCodeCamp.

Programming is also very important for robotics. Yes, robots are programmed too!

Robotics is defined by Britannica as the:

Design, construction, and use of machines (robots) to perform tasks done traditionally by human beings.

Robots are just like computers. They do not know what to do until you tell them what to do by writing instructions in your programs. If you learn how to code, you can program robots and industrial machinery found in manufacturing facilities.

If you are interested in learning how to code for robotics, electronics, and related fields, this is a free course on Arduino on freeCodeCamp's YouTube channel:

• Arduino Course for Beginners - Open-Source Electronics Platform

Other Applications

There are many other fascinating applications of programming in almost every field. These are some highlights:

• Agriculture: in this article by MIT News, a farmer developed an autonomous tractor app after learning how to code.
• Self-driving cars: autonomous cars rely on software to analyze their surroundings and to make quick and accurate decisions on the road. If you are interested in this area, this is a course on this topic on freeCodeCamp's YouTube channel.
• Finance: programming can also be helpful to develop programs and models that predict financial indicators and trends. For example, this is a course on algorithmic trading on freeCodeCamp's YouTube channel.

The possibilities are endless. I hope that this section will give you a notion of why learning how to code is so important for your present and for your future. It will be a valuable skill to have in any field you choose.

Awesome. Now let's dive into the soft skills that you need to become a successful programmer.

🔹 Skills of a Successful Programmer

After going through the diverse range of applications of programming, you must be curious to know what skills are needed to succeed in this field.

A programmer should be curious. Whether you are just starting to learn how to code or you already have 20 years of experience, coding projects will always present you with new challenges and learning opportunities. If you take these opportunities, you will continously improve your skills and succeed.

Enthusiasm is a key trait of a successful programmer but this applies in general to any field if you want to succeed. Enthusiasm will keep you happy and curious about what you are creating and learning.

💡 Tip: If you ever feel like you are not as enthusiastic as you used to be, it's time to find or learn something new that can light the spark in you again and fill you with hope and dreams.

A programmer must be patient because transforming an initial idea into a working product can take time, effort, and many different steps. Patience will keep you focused on your final goal.  

Programming can be challenging. That is true. But what defines you is not how many challenges you face, it's how you face them. If you thrive despite these challenges, you will become a better programmer and you could create something that could change the world.

Programmers must be creative because even though every programming language has a particular set of rules for writing the code, coding is like using LEGOs. You have the building-blocks but you need to decide what to create and how to create it. The process of writing the code requires creativity while following the established best practices.

Problem-solving and Analysis

Programming is basically analyzing and solving problems with code. Depending on your field of choice, those problems will be simpler or more complex but they will all require some level of problem-solving skills and a thorough analysis of the situation.

Questions like:

• What should I build?
• How can I build it?
• What is the best way to build this?

Are part of the everyday routine of a programmer.

Ability to Focus for Long Periods of Time

When you are working on a coding project, you will need to focus on a task for long periods of time. From creating the design, to planning and writing the code, to testing the result, and to fixing bugs (issues with the code), you will dedicate many hours to a particular task. This is why it's essential to be able to focus and to keep your final goal in mind.

Taking Detailed Notes

This skill is very important for programmers, particularly when you are learning how to code. Taking detailed notes can be help you to understand and remember the concepts and tools you learn. This also applies for experienced programmers, since being a programmer involves life-long learning.

Communication

Initially, you might think that programming is a solitary activity and imagine that a programmer spends hundreds of hours alone sitting on a desk.

But the reality is that when you find your first job, you will see that communication is super important to coordinate tasks with other team members and to exchange ideas and feedback.

Open to Feedback

In programming, there is usually more than one way to implement the same functionality. Different alternatives may work similarly, but some may be easier to read or more efficient in terms of time or resource consumption.

When you are learning how to code, you should always take constructive feedback as a tool for learning. Similarly, when you are working on a team, take your colleagues' feedback positively and always try to improve.

Life-long Learning

Programming equals life-long learning. If you are interested in learning how to code, you must know that you will always need to be learning new things as new technologies emerge and existing technologies are updated. Think about it... that is great because there is always something interesting and new to learn!

Open to Trying New Things

Finally, an essential skill to be a successful programmer is to be open to trying new things. Step out of your comfort zone and be open to new technologies and products. In the technology industry, things evolve very quickly and adapting to change is essential.

🔸 Tips for Learning How to Code

Now that you know more about programming, programming languages, and the skills you need to be a successful programmer, let's see some tips for learning how to code.

💡 Tip: these tips are based on my personal experience and opinions.

• Choose one programming language to learn first. When you are learning how to code, it's easy to feel overwhelmed with the number of options and entry paths. My advice would be to focus on understanding the essential computer science concepts and one programming language first. Python and JavaScript are great options to start learning the fundamentals.
• Take detailed notes. Note-taking skills are essential to record and to analyze the topics you are learning. You can add custom comments and annotations to explain what you are learning.
• Practice constantly. You can only improve your problem-solving skills by practicing and by learning new techniques and tools. Try to practice every day.

💡 Tip: There is a challenge called the #100DaysOfCode challenge that you can join to practice every day.  

• Always try again. If you can't solve a problem on your first try, take a break and come back again and again until you solve it. That is the only way to learn. Learn from your mistakes and learn new approaches.
• Learn how to research and how to find answers. Programming languages, libraries, and frameworks usually have official documentations that explain their built-in elements and tools and how you can use them. This is a precious resource that you should definitely refer to.
• Browse Stack Overflow . This is an amazing platform. It is like an online encyclopedia of answers to common programming questions. You can find answers to existing questions and ask new questions to get help from the community.
• Set goals. Motivation is one of the most important factors for success. Setting goals is very important to keep you focused, motivated, and enthusiastic. Once you reach your goals, set new ones that you find challenging and exciting.
• Create projects. When you are learning how to code, applying your skills will help you to expand your knowledge and remember things better. Creating projects is the perfect way to practice and to create a portfolio that you can show to potential employers.

🔹 Basic Programming Concepts

Great. If reading this article has helped you confirm that you want to learn programming, let's take your first steps.

These are some basic programming concepts that you should know:

• Variable: a variable is a name that we assign to a value in a computer program. When we define a variable, we assign a value to a name and we allocate a space in memory to store that value. The value of a variable can be updated during the program.
• Constant: a constant is similar to a variable. It stores a value but it cannot be modified. Once you assign a value to a constant, you cannot change it during the entire program.
• Conditional: a conditional is a programming structure that lets developers choose what the computer should do based on a condition. If the condition is True, something will happen but if the condition is False, something different can happen.
• Loop: a loop is a programming structure that let us run a code block (a sequence of instructions) multiple times. They are super helpful to avoid code repetition and to implement more complex functionality.
• Function: a function helps us to avoid code repetition and to reuse our code. It is like a code block to which we assign a name but it also has some special characteristics. We can write the name of the function to run that sequence of instructions without writing them again.

💡 Tip: Functions can communicate with main programs and main programs can communicate with functions through parameters , arguments , and return statements.

• Class: a class is used as a blueprint to define the characteristics and functionality of a type of object. Just like we have objects in our real world, we can represent objects in our programs.
• Bug: a bug is an error in the logic or implementation of a program that results in an unexpected or incorrect output.
• Debugging: debugging is the process of finding and fixing bugs in a program.
• IDE: this acronym stands for Integrated Development Environment. It is a software development environment that has the most helpful tools that you will need to write computer programs such as a file editor, an explorer, a terminal, and helpful menu options.

💡 Tip: a commonly used and free IDE is Visual Studio Code , created by Microsoft.

Awesome! Now you know some of the fundamental concepts in programming. Like you learned, each programming language has a different syntax, but they all share most of these programming structures and concepts.  

🔸 Types of Programming Languages

Programming languages can be classified based on different criteria. If you want to learn how to code, it's important for you to learn these basic classifications:

• High-level programming languages: they are designed to be understood by humans and they have to be converted into machine code before the computer can understand them. They are the programming languages that we commonly use. For example: JavaScript, Python, Java, C#, C++, and Kotlin.
• Low-level programming languages: they are more difficult to understand because they are not designed for humans. They are designed to be understood and processed efficiently by machines.

Conversion into Machine Code

• Compiled programming languages: programs written with this type of programming language are converted directly into machine code by a compiler. Examples include C, C++, Haskell, and Go.
• Interpreted programming languages: programs written with this type of programming language rely on another program called the interpreter, which is in charge of running the code line by line. Examples include Python, JavaScript, PHP, and Ruby.

💡 Tip: according to this article on freeCodeCamp's publication:

Most programming languages can have both compiled and interpreted implementations – the language itself is not necessarily compiled or interpreted. However, for simplicity’s sake, they’re typically referred to as such.

There are other types of programming languages based on different criteria, such as:

• Procedural programming languages
• Functional programming languages
• Object-oriented programming languages
• Scripting languages
• Logic programming languages

And the list of types of programming languages continues. This is very interesting because you can analyze the characteristics of a programming language to help you choose the right one for your project.

🔹 How to Contribute to Open Source Projects

Finally, you might think that coding implies sitting at a desk for many hours looking at your code without any human interaction. But let me tell you that this does not have to be true at all. You can be part of a learning community or a developer community.

Initially, when you are learning how to code, you can participate in a learning community like freeCodeCamp. This way, you will share your journey with others who are learning how to code, just like you.

Then, when you have enough skills and confidence in your knowledge, you can practice by contributing to open source projects and join developer communities.

Open source software is defined by Opensource.com as:

Software with source code that anyone can inspect, modify, and enhance.

GitHub is an online platform for hosting projects with version control. There, you can find many open source projects (like freeCodeCamp ) that you can contribute to and practice your skills.

💡 Tip: many open source projects welcome first-time contributions and contributions from all skill levels. These are great opportunities to practice your skills and to contribute to real-world projects.  

Contributing to open source projects on GitHub is great to acquire new experience working and communicating with other developers. This is another important skill for finding a job in this field.

Working on a team is a great experience. I totally recommend it once you feel comfortable enough with your skills and knowledge.

You did it! You reached the end of this article. Great work. Now you know what programming is all about. Let's see a brief summary.

🔸 In Summary

• Programming is a very powerful skill. If you learn how to code, you can make your vision come true.
• Programming has many different applications in many different fields. You can find an application for programming in basically any field you choose.
• Programming languages can be classified based on different criteria and they share basic concepts such as variables, conditionals, loops, and functions.
• Always set goals and take detailed notes. To succeed as a programmer, you need to be enthusiastic and consistent.

Thank you very much for reading my article. I hope you liked it and found it helpful. Now you know why you should learn how to code.

🔅 I invite you to follow me on Twitter ( @EstefaniaCassN ) and YouTube ( Coding with Estefania ) to find coding tutorials.

Developer, technical writer, and content creator @freeCodeCamp. I run the freeCodeCamp.org Español YouTube channel.

If you read this far, thank the author to show them you care. Say Thanks

Learn to code for free. freeCodeCamp's open source curriculum has helped more than 40,000 people get jobs as developers. Get started

Help | Advanced Search

Computer Science > Software Engineering

Title: paths to testing: why women enter and remain in software testing.

Abstract: Background. Women bring unique problem-solving skills to software development, often favoring a holistic approach and attention to detail. In software testing, precision and attention to detail are essential as professionals explore system functionalities to identify defects. Recognizing the alignment between these skills and women's strengths can derive strategies for enhancing diversity in software engineering. Goal. This study investigates the motivations behind women choosing careers in software testing, aiming to provide insights into their reasons for entering and remaining in the field. Method. This study used a cross-sectional survey methodology following established software engineering guidelines, collecting data from women in software testing to explore their motivations, experiences, and perspectives. Findings. The findings reveal that women enter software testing due to increased entry-level job opportunities, work-life balance, and even fewer gender stereotypes. Their motivations to stay include the impact of delivering high-quality software, continuous learning opportunities, and the challenges the activities bring to them. However, inclusiveness and career development in the field need improvement for sustained diversity. Conclusion. Preliminary yet significant, these findings offer interesting insights for researchers and practitioners towards the understanding of women's diverse motivations in software testing and how this understanding is important for fostering professional growth and creating a more inclusive and equitable industry landscape.

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .