computer problem solving skills

How to think like a programmer — lessons in problem solving

by Richard Reis

If you’re interested in programming, you may well have seen this quote before:

“Everyone in this country should learn to program a computer, because it teaches you to think.” — Steve Jobs

You probably also wondered what does it mean, exactly, to think like a programmer? And how do you do it??

Essentially, it’s all about a more effective way for problem solving .

In this post, my goal is to teach you that way.

By the end of it, you’ll know exactly what steps to take to be a better problem-solver.

Why is this important?

Problem solving is the meta-skill.

We all have problems. Big and small. How we deal with them is sometimes, well…pretty random.

Unless you have a system, this is probably how you “solve” problems (which is what I did when I started coding):

• Try a solution.
• If that doesn’t work, try another one.
• If that doesn’t work, repeat step 2 until you luck out.

Look, sometimes you luck out. But that is the worst way to solve problems! And it’s a huge, huge waste of time.

The best way involves a) having a framework and b) practicing it.

“Almost all employers prioritize problem-solving skills first.

Problem-solving skills are almost unanimously the most important qualification that employers look for….more than programming languages proficiency, debugging, and system design.

Demonstrating computational thinking or the ability to break down large, complex problems is just as valuable (if not more so) than the baseline technical skills required for a job.” — Hacker Rank ( 2018 Developer Skills Report )

Have a framework

To find the right framework, I followed the advice in Tim Ferriss’ book on learning, “ The 4-Hour Chef ”.

It led me to interview two really impressive people: C. Jordan Ball (ranked 1st or 2nd out of 65,000+ users on Coderbyte ), and V. Anton Spraul (author of the book “ Think Like a Programmer: An Introduction to Creative Problem Solving ”).

I asked them the same questions, and guess what? Their answers were pretty similar!

Soon, you too will know them.

Sidenote: this doesn’t mean they did everything the same way. Everyone is different. You’ll be different. But if you start with principles we all agree are good, you’ll get a lot further a lot quicker.

“The biggest mistake I see new programmers make is focusing on learning syntax instead of learning how to solve problems.” — V. Anton Spraul

So, what should you do when you encounter a new problem?

Here are the steps:

1. Understand

Know exactly what is being asked. Most hard problems are hard because you don’t understand them (hence why this is the first step).

How to know when you understand a problem? When you can explain it in plain English.

Do you remember being stuck on a problem, you start explaining it, and you instantly see holes in the logic you didn’t see before?

Most programmers know this feeling.

This is why you should write down your problem, doodle a diagram, or tell someone else about it (or thing… some people use a rubber duck ).

“If you can’t explain something in simple terms, you don’t understand it.” — Richard Feynman

Don’t dive right into solving without a plan (and somehow hope you can muddle your way through). Plan your solution!

Nothing can help you if you can’t write down the exact steps.

In programming, this means don’t start hacking straight away. Give your brain time to analyze the problem and process the information.

To get a good plan, answer this question:

“Given input X, what are the steps necessary to return output Y?”

Sidenote: Programmers have a great tool to help them with this… Comments!

Pay attention. This is the most important step of all.

Do not try to solve one big problem. You will cry.

Instead, break it into sub-problems. These sub-problems are much easier to solve.

Then, solve each sub-problem one by one. Begin with the simplest. Simplest means you know the answer (or are closer to that answer).

After that, simplest means this sub-problem being solved doesn’t depend on others being solved.

Once you solved every sub-problem, connect the dots.

Connecting all your “sub-solutions” will give you the solution to the original problem. Congratulations!

This technique is a cornerstone of problem-solving. Remember it (read this step again, if you must).

“If I could teach every beginning programmer one problem-solving skill, it would be the ‘reduce the problem technique.’

For example, suppose you’re a new programmer and you’re asked to write a program that reads ten numbers and figures out which number is the third highest. For a brand-new programmer, that can be a tough assignment, even though it only requires basic programming syntax.

If you’re stuck, you should reduce the problem to something simpler. Instead of the third-highest number, what about finding the highest overall? Still too tough? What about finding the largest of just three numbers? Or the larger of two?

Reduce the problem to the point where you know how to solve it and write the solution. Then expand the problem slightly and rewrite the solution to match, and keep going until you are back where you started.” — V. Anton Spraul

By now, you’re probably sitting there thinking “Hey Richard... That’s cool and all, but what if I’m stuck and can’t even solve a sub-problem??”

First off, take a deep breath. Second, that’s fair.

Don’t worry though, friend. This happens to everyone!

The difference is the best programmers/problem-solvers are more curious about bugs/errors than irritated.

In fact, here are three things to try when facing a whammy:

• Debug: Go step by step through your solution trying to find where you went wrong. Programmers call this debugging (in fact, this is all a debugger does).

“The art of debugging is figuring out what you really told your program to do rather than what you thought you told it to do.”” — Andrew Singer

• Reassess: Take a step back. Look at the problem from another perspective. Is there anything that can be abstracted to a more general approach?

“Sometimes we get so lost in the details of a problem that we overlook general principles that would solve the problem at a more general level. […]

The classic example of this, of course, is the summation of a long list of consecutive integers, 1 + 2 + 3 + … + n, which a very young Gauss quickly recognized was simply n(n+1)/2, thus avoiding the effort of having to do the addition.” — C. Jordan Ball

Sidenote: Another way of reassessing is starting anew. Delete everything and begin again with fresh eyes. I’m serious. You’ll be dumbfounded at how effective this is.

• Research: Ahh, good ol’ Google. You read that right. No matter what problem you have, someone has probably solved it. Find that person/ solution. In fact, do this even if you solved the problem! (You can learn a lot from other people’s solutions).

Caveat: Don’t look for a solution to the big problem. Only look for solutions to sub-problems. Why? Because unless you struggle (even a little bit), you won’t learn anything. If you don’t learn anything, you wasted your time.

Don’t expect to be great after just one week. If you want to be a good problem-solver, solve a lot of problems!

Practice. Practice. Practice. It’ll only be a matter of time before you recognize that “this problem could easily be solved with <insert concept here>.”

How to practice? There are options out the wazoo!

Chess puzzles, math problems, Sudoku, Go, Monopoly, video-games, cryptokitties, bla… bla… bla….

In fact, a common pattern amongst successful people is their habit of practicing “micro problem-solving.” For example, Peter Thiel plays chess, and Elon Musk plays video-games.

“Byron Reeves said ‘If you want to see what business leadership may look like in three to five years, look at what’s happening in online games.’

Fast-forward to today. Elon [Musk], Reid [Hoffman], Mark Zuckerberg and many others say that games have been foundational to their success in building their companies.” — Mary Meeker ( 2017 internet trends report )

Does this mean you should just play video-games? Not at all.

But what are video-games all about? That’s right, problem-solving!

So, what you should do is find an outlet to practice. Something that allows you to solve many micro-problems (ideally, something you enjoy).

For example, I enjoy coding challenges. Every day, I try to solve at least one challenge (usually on Coderbyte ).

Like I said, all problems share similar patterns.

That’s all folks!

Now, you know better what it means to “think like a programmer.”

You also know that problem-solving is an incredible skill to cultivate (the meta-skill).

As if that wasn’t enough, notice how you also know what to do to practice your problem-solving skills!

Phew… Pretty cool right?

Finally, I wish you encounter many problems.

You read that right. At least now you know how to solve them! (also, you’ll learn that with every solution, you improve).

“Just when you think you’ve successfully navigated one obstacle, another emerges. But that’s what keeps life interesting.[…]

Life is a process of breaking through these impediments — a series of fortified lines that we must break through.

Each time, you’ll learn something.

Each time, you’ll develop strength, wisdom, and perspective.

Each time, a little more of the competition falls away. Until all that is left is you: the best version of you.” — Ryan Holiday ( The Obstacle is the Way )

Now, go solve some problems!

And best of luck ?

Special thanks to C. Jordan Ball and V. Anton Spraul . All the good advice here came from them.

Thanks for reading! If you enjoyed it, test how many times can you hit in 5 seconds. It’s great cardio for your fingers AND will help other people see the story.

If this article was helpful, share it .

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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.

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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.

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Exploring the Problem Solving Cycle in Computer Science – Strategies, Techniques, and Tools

• Post author By bicycle-u
• Post date 08.12.2023

The world of computer science is built on the foundation of problem solving. Whether it’s finding a solution to a complex algorithm or analyzing data to make informed decisions, the problem solving cycle is at the core of every computer science endeavor.

At its essence, problem solving in computer science involves breaking down a complex problem into smaller, more manageable parts. This allows for a systematic approach to finding a solution by analyzing each part individually. The process typically starts with gathering and understanding the data or information related to the problem at hand.

Once the data is collected, computer scientists use various techniques and algorithms to analyze and explore possible solutions. This involves evaluating different approaches and considering factors such as efficiency, accuracy, and scalability. During this analysis phase, it is crucial to think critically and creatively to come up with innovative solutions.

After a thorough analysis, the next step in the problem solving cycle is designing and implementing a solution. This involves creating a detailed plan of action, selecting the appropriate tools and technologies, and writing the necessary code to bring the solution to life. Attention to detail and precision are key in this stage to ensure that the solution functions as intended.

The final step in the problem solving cycle is evaluating the solution and its effectiveness. This includes testing the solution against different scenarios and data sets to ensure its reliability and performance. If any issues or limitations are discovered, adjustments and optimizations are made to improve the solution.

In conclusion, the problem solving cycle is a fundamental process in computer science, involving analysis, data exploration, algorithm development, solution implementation, and evaluation. It is through this cycle that computer scientists are able to tackle complex problems and create innovative solutions that drive progress in the field of computer science.

Understanding the Importance

In computer science, problem solving is a crucial skill that is at the core of the problem solving cycle. The problem solving cycle is a systematic approach to analyzing and solving problems, involving various stages such as problem identification, analysis, algorithm design, implementation, and evaluation. Understanding the importance of this cycle is essential for any computer scientist or programmer.

Data Analysis and Algorithm Design

The first step in the problem solving cycle is problem identification, which involves recognizing and defining the issue at hand. Once the problem is identified, the next crucial step is data analysis. This involves gathering and examining relevant data to gain insights and understand the problem better. Data analysis helps in identifying patterns, trends, and potential solutions.

After data analysis, the next step is algorithm design. An algorithm is a step-by-step procedure or set of rules to solve a problem. Designing an efficient algorithm is crucial as it determines the effectiveness and efficiency of the solution. A well-designed algorithm takes into consideration the constraints, resources, and desired outcomes while implementing the solution.

Implementation and Evaluation

Once the algorithm is designed, the next step in the problem solving cycle is implementation. This involves translating the algorithm into a computer program using a programming language. The implementation phase requires coding skills and expertise in a specific programming language.

After implementation, the solution needs to be evaluated to ensure that it solves the problem effectively. Evaluation involves testing the program and verifying its correctness and efficiency. This step is critical to identify any errors or issues and to make necessary improvements or adjustments.

In conclusion, understanding the importance of the problem solving cycle in computer science is essential for any computer scientist or programmer. It provides a systematic and structured approach to analyze and solve problems, ensuring efficient and effective solutions. By following the problem solving cycle, computer scientists can develop robust algorithms, implement them in efficient programs, and evaluate their solutions to ensure their correctness and efficiency.

Identifying the Problem

In the problem solving cycle in computer science, the first step is to identify the problem that needs to be solved. This step is crucial because without a clear understanding of the problem, it is impossible to find a solution.

Identification of the problem involves a thorough analysis of the given data and understanding the goals of the task at hand. It requires careful examination of the problem statement and any constraints or limitations that may affect the solution.

During the identification phase, the problem is broken down into smaller, more manageable parts. This can involve breaking the problem down into sub-problems or identifying the different aspects or components that need to be addressed.

Identifying the problem also involves considering the resources and tools available for solving it. This may include considering the specific tools and programming languages that are best suited for the problem at hand.

By properly identifying the problem, computer scientists can ensure that they are focused on the right goals and are better equipped to find an effective and efficient solution. It sets the stage for the rest of the problem solving cycle, including the analysis, design, implementation, and evaluation phases.

Gathering the Necessary Data

Before finding a solution to a computer science problem, it is essential to gather the necessary data. Whether it’s writing a program or developing an algorithm, data serves as the backbone of any solution. Without proper data collection and analysis, the problem-solving process can become inefficient and ineffective.

The Importance of Data

In computer science, data is crucial for a variety of reasons. First and foremost, it provides the information needed to understand and define the problem at hand. By analyzing the available data, developers and programmers can gain insights into the nature of the problem and determine the most efficient approach for solving it.

Additionally, data allows for the evaluation of potential solutions. By collecting and organizing relevant data, it becomes possible to compare different algorithms or strategies and select the most suitable one. Data also helps in tracking progress and measuring the effectiveness of the chosen solution.

Data Gathering Process

The process of gathering data involves several steps. Firstly, it is necessary to identify the type of data needed for the particular problem. This may include numerical values, textual information, or other types of data. It is important to determine the sources of data and assess their reliability.

Once the required data has been identified, it needs to be collected. This can be done through various methods, such as surveys, experiments, observations, or by accessing existing data sets. The collected data should be properly organized, ensuring its accuracy and validity.

Data cleaning and preprocessing are vital steps in the data gathering process. This involves removing any irrelevant or erroneous data and transforming it into a suitable format for analysis. Properly cleaned and preprocessed data will help in generating reliable and meaningful insights.

Data Analysis and Interpretation

After gathering and preprocessing the data, the next step is data analysis and interpretation. This involves applying various statistical and analytical methods to uncover patterns, trends, and relationships within the data. By analyzing the data, programmers can gain valuable insights that can inform the development of an effective solution.

During the data analysis process, it is crucial to remain objective and unbiased. The analysis should be based on sound reasoning and logical thinking. It is also important to communicate the findings effectively, using visualizations or summaries to convey the information to stakeholders or fellow developers.

In conclusion, gathering the necessary data is a fundamental step in solving computer science problems. It provides the foundation for understanding the problem, evaluating potential solutions, and tracking progress. By following a systematic and rigorous approach to data gathering and analysis, developers can ensure that their solutions are efficient, effective, and well-informed.

Analyzing the Data

Once you have collected the necessary data, the next step in the problem-solving cycle is to analyze it. Data analysis is a crucial component of computer science, as it helps us understand the problem at hand and develop effective solutions.

To analyze the data, you need to break it down into manageable pieces and examine each piece closely. This process involves identifying patterns, trends, and outliers that may be present in the data. By doing so, you can gain insights into the problem and make informed decisions about the best course of action.

There are several techniques and tools available for data analysis in computer science. Some common methods include statistical analysis, data visualization, and machine learning algorithms. Each approach has its own strengths and limitations, so it’s essential to choose the most appropriate method for the problem you are solving.

Statistical Analysis

Statistical analysis involves using mathematical models and techniques to analyze data. It helps in identifying correlations, distributions, and other statistical properties of the data. By applying statistical tests, you can determine the significance and validity of your findings.

Data Visualization

Data visualization is the process of presenting data in a visual format, such as charts, graphs, or maps. It allows for a better understanding of complex data sets and facilitates the communication of findings. Through data visualization, patterns and trends can become more apparent, making it easier to derive meaningful insights.

Machine Learning Algorithms

Machine learning algorithms are powerful tools for analyzing large and complex data sets. These algorithms can automatically detect patterns and relationships in the data, leading to the development of predictive models and solutions. By training the algorithm on a labeled dataset, it can learn from the data and make accurate predictions or classifications.

In conclusion, analyzing the data is a critical step in the problem-solving cycle in computer science. It helps us gain a deeper understanding of the problem and develop effective solutions. Whether through statistical analysis, data visualization, or machine learning algorithms, data analysis plays a vital role in transforming raw data into actionable insights.

Exploring Possible Solutions

Once you have gathered data and completed the analysis, the next step in the problem-solving cycle is to explore possible solutions. This is where the true power of computer science comes into play. With the use of algorithms and the application of scientific principles, computer scientists can develop innovative solutions to complex problems.

During this stage, it is important to consider a variety of potential solutions. This involves brainstorming different ideas and considering their feasibility and potential effectiveness. It may be helpful to consult with colleagues or experts in the field to gather additional insights and perspectives.

Developing an Algorithm

One key aspect of exploring possible solutions is the development of an algorithm. An algorithm is a step-by-step set of instructions that outlines a specific process or procedure. In the context of problem solving in computer science, an algorithm provides a clear roadmap for implementing a solution.

The development of an algorithm requires careful thought and consideration. It is important to break down the problem into smaller, manageable steps and clearly define the inputs and outputs of each step. This allows for the creation of a logical and efficient solution.

Evaluating the Solutions

Once you have developed potential solutions and corresponding algorithms, the next step is to evaluate them. This involves analyzing each solution to determine its strengths, weaknesses, and potential impact. Consider factors such as efficiency, scalability, and resource requirements.

It may be helpful to conduct experiments or simulations to further assess the effectiveness of each solution. This can provide valuable insights and data to support the decision-making process.

Ultimately, the goal of exploring possible solutions is to find the most effective and efficient solution to the problem at hand. By leveraging the power of data, analysis, algorithms, and scientific principles, computer scientists can develop innovative solutions that drive progress and solve complex problems in the world of technology.

Evaluating the Options

Once you have identified potential solutions and algorithms for a problem, the next step in the problem-solving cycle in computer science is to evaluate the options. This evaluation process involves analyzing the potential solutions and algorithms based on various criteria to determine the best course of action.

Consider the Problem

Before evaluating the options, it is important to take a step back and consider the problem at hand. Understand the requirements, constraints, and desired outcomes of the problem. This analysis will help guide the evaluation process.

Analyze the Options

Next, it is crucial to analyze each solution or algorithm option individually. Look at factors such as efficiency, accuracy, ease of implementation, and scalability. Consider whether the solution or algorithm meets the specific requirements of the problem, and if it can be applied to related problems in the future.

Additionally, evaluate the potential risks and drawbacks associated with each option. Consider factors such as cost, time, and resources required for implementation. Assess any potential limitations or trade-offs that may impact the overall effectiveness of the solution or algorithm.

Select the Best Option

Based on the analysis, select the best option that aligns with the specific problem-solving goals. This may involve prioritizing certain criteria or making compromises based on the limitations identified during the evaluation process.

Remember that the best option may not always be the most technically complex or advanced solution. Consider the practicality and feasibility of implementation, as well as the potential impact on the overall system or project.

In conclusion, evaluating the options is a critical step in the problem-solving cycle in computer science. By carefully analyzing the potential solutions and algorithms, considering the problem requirements, and considering the limitations and trade-offs, you can select the best option to solve the problem at hand.

Making a Decision

Decision-making is a critical component in the problem-solving process in computer science. Once you have analyzed the problem, identified the relevant data, and generated a potential solution, it is important to evaluate your options and choose the best course of action.

Consider All Factors

When making a decision, it is important to consider all relevant factors. This includes evaluating the potential benefits and drawbacks of each option, as well as understanding any constraints or limitations that may impact your choice.

In computer science, this may involve analyzing the efficiency of different algorithms or considering the scalability of a proposed solution. It is important to take into account both the short-term and long-term impacts of your decision.

Weigh the Options

Once you have considered all the factors, it is important to weigh the options and determine the best approach. This may involve assigning weights or priorities to different factors based on their importance.

Using techniques such as decision matrices or cost-benefit analysis can help you systematically compare and evaluate different options. By quantifying and assessing the potential risks and rewards, you can make a more informed decision.

Remember: Decision-making in computer science is not purely subjective or based on personal preference. It is crucial to use analytical and logical thinking to select the most optimal solution.

In conclusion, making a decision is a crucial step in the problem-solving process in computer science. By considering all relevant factors and weighing the options using logical analysis, you can choose the best possible solution to a given problem.

Implementing the Solution

Once the problem has been analyzed and a solution has been proposed, the next step in the problem-solving cycle in computer science is implementing the solution. This involves turning the proposed solution into an actual computer program or algorithm that can solve the problem.

In order to implement the solution, computer science professionals need to have a strong understanding of various programming languages and data structures. They need to be able to write code that can manipulate and process data in order to solve the problem at hand.

During the implementation phase, the proposed solution is translated into a series of steps or instructions that a computer can understand and execute. This involves breaking down the problem into smaller sub-problems and designing algorithms to solve each sub-problem.

Computer scientists also need to consider the efficiency of their solution during the implementation phase. They need to ensure that the algorithm they design is able to handle large amounts of data and solve the problem in a reasonable amount of time. This often requires optimization techniques and careful consideration of the data structures used.

Once the code has been written and the algorithm has been implemented, it is important to test and debug the solution. This involves running test cases and checking the output to ensure that the program is working correctly. If any errors or bugs are found, they need to be fixed before the solution can be considered complete.

In conclusion, implementing the solution is a crucial step in the problem-solving cycle in computer science. It requires strong programming skills and a deep understanding of algorithms and data structures. By carefully designing and implementing the solution, computer scientists can solve problems efficiently and effectively.

Testing and Debugging

In computer science, testing and debugging are critical steps in the problem-solving cycle. Testing helps ensure that a program or algorithm is functioning correctly, while debugging analyzes and resolves any issues or bugs that may arise.

Testing involves running a program with specific input data to evaluate its output. This process helps verify that the program produces the expected results and handles different scenarios correctly. It is important to test both the normal and edge cases to ensure the program’s reliability.

Debugging is the process of identifying and fixing errors or bugs in a program. When a program does not produce the expected results or crashes, it is necessary to go through the code to find and fix the problem. This can involve analyzing the program’s logic, checking for syntax errors, and using debugging tools to trace the flow of data and identify the source of the issue.

Data analysis plays a crucial role in both testing and debugging. It helps to identify patterns, anomalies, or inconsistencies in the program’s behavior. By analyzing the data, developers can gain insights into potential issues and make informed decisions on how to improve the program’s performance.

In conclusion, testing and debugging are integral parts of the problem-solving cycle in computer science. Through testing and data analysis, developers can verify the correctness of their programs and identify and resolve any issues that may arise. This ensures that the algorithms and programs developed in computer science are robust, reliable, and efficient.

Iterating for Improvement

In computer science, problem solving often involves iterating through multiple cycles of analysis, solution development, and evaluation. This iterative process allows for continuous improvement in finding the most effective solution to a given problem.

The problem solving cycle starts with problem analysis, where the specific problem is identified and its requirements are understood. This step involves examining the problem from various angles and gathering all relevant information.

Once the problem is properly understood, the next step is to develop an algorithm or a step-by-step plan to solve the problem. This algorithm is a set of instructions that, when followed correctly, will lead to the solution.

After the algorithm is developed, it is implemented in a computer program. This step involves translating the algorithm into a programming language that a computer can understand and execute.

Once the program is implemented, it is then tested and evaluated to ensure that it produces the correct solution. This evaluation step is crucial in identifying any errors or inefficiencies in the program and allows for further improvement.

If any issues or problems are found during testing, the cycle iterates, starting from problem analysis again. This iterative process allows for refinement and improvement of the solution until the desired results are achieved.

Iterating for improvement is a fundamental concept in computer science problem solving. By continually analyzing, developing, and evaluating solutions, computer scientists are able to find the most optimal and efficient approaches to solving problems.

Documenting the Process

Documenting the problem-solving process in computer science is an essential step to ensure that the cycle is repeated successfully. The process involves gathering information, analyzing the problem, and designing a solution.

During the analysis phase, it is crucial to identify the specific problem at hand and break it down into smaller components. This allows for a more targeted approach to finding the solution. Additionally, analyzing the data involved in the problem can provide valuable insights and help in designing an effective solution.

Once the analysis is complete, it is important to document the findings. This documentation can take various forms, such as written reports, diagrams, or even code comments. The goal is to create a record that captures the problem, the analysis, and the proposed solution.

Documenting the process serves several purposes. Firstly, it allows for easy communication and collaboration between team members or future developers. By documenting the problem, analysis, and solution, others can easily understand the thought process behind the solution and potentially build upon it.

Secondly, documenting the process provides an opportunity for reflection and improvement. By reviewing the documentation, developers can identify areas where the problem-solving cycle can be strengthened or optimized. This continuous improvement is crucial in the field of computer science, as new challenges and technologies emerge rapidly.

In conclusion, documenting the problem-solving process is an integral part of the computer science cycle. It allows for effective communication, collaboration, and reflection on the solutions devised. By taking the time to document the process, developers can ensure a more efficient and successful problem-solving experience.

Communicating the Solution

Once the problem solving cycle is complete, it is important to effectively communicate the solution. This involves explaining the analysis, data, and steps taken to arrive at the solution.

Analyzing the Problem

During the problem solving cycle, a thorough analysis of the problem is conducted. This includes understanding the problem statement, gathering relevant data, and identifying any constraints or limitations. It is important to clearly communicate this analysis to ensure that others understand the problem at hand.

Presenting the Solution

The next step in communicating the solution is presenting the actual solution. This should include a detailed explanation of the steps taken to solve the problem, as well as any algorithms or data structures used. It is important to provide clear and concise descriptions of the solution, so that others can understand and reproduce the results.

Overall, effective communication of the solution in computer science is essential to ensure that others can understand and replicate the problem solving process. By clearly explaining the analysis, data, and steps taken, the solution can be communicated in a way that promotes understanding and collaboration within the field of computer science.

Reflecting and Learning

Reflecting and learning are crucial steps in the problem solving cycle in computer science. Once a problem has been solved, it is essential to reflect on the entire process and learn from the experience. This allows for continuous improvement and growth in the field of computer science.

During the reflecting phase, one must analyze and evaluate the problem solving process. This involves reviewing the initial problem statement, understanding the constraints and requirements, and assessing the effectiveness of the chosen algorithm and solution. It is important to consider the efficiency and accuracy of the solution, as well as any potential limitations or areas for optimization.

By reflecting on the problem solving cycle, computer scientists can gain valuable insights into their own strengths and weaknesses. They can identify areas where they excelled and areas where improvement is needed. This self-analysis helps in honing problem solving skills and becoming a better problem solver.

Learning from Mistakes

Mistakes are an integral part of the problem solving cycle, and they provide valuable learning opportunities. When a problem is not successfully solved, it is essential to analyze the reasons behind the failure and learn from them. This involves identifying errors in the algorithm or solution, understanding the underlying concepts or principles that were misunderstood, and finding alternative approaches or strategies.

Failure should not be seen as a setback, but rather as an opportunity for growth. By learning from mistakes, computer scientists can improve their problem solving abilities and expand their knowledge and understanding of computer science. It is through these failures and the subsequent learning process that new ideas and innovations are often born.

Continuous Improvement

Reflecting and learning should not be limited to individual problem solving experiences, but should be an ongoing practice. As computer science is a rapidly evolving field, it is crucial to stay updated with new technologies, algorithms, and problem solving techniques. Continuous learning and improvement contribute to staying competitive and relevant in the field.

Computer scientists can engage in continuous improvement by seeking feedback from peers, participating in research and development activities, attending conferences and workshops, and actively seeking new challenges and problem solving opportunities. This dedication to learning and improvement ensures that one’s problem solving skills remain sharp and effective.

In conclusion, reflecting and learning are integral parts of the problem solving cycle in computer science. They enable computer scientists to refine their problem solving abilities, learn from mistakes, and continuously improve their skills and knowledge. By embracing these steps, computer scientists can stay at the forefront of the ever-changing world of computer science and contribute to its advancements.

Applying Problem Solving in Real Life

In computer science, problem solving is not limited to the realm of programming and algorithms. It is a skill that can be applied to various aspects of our daily lives, helping us to solve problems efficiently and effectively. By using the problem-solving cycle and applying the principles of analysis, data, solution, algorithm, and cycle, we can tackle real-life challenges with confidence and success.

The first step in problem-solving is to analyze the problem at hand. This involves breaking it down into smaller, more manageable parts and identifying the key issues or goals. By understanding the problem thoroughly, we can gain insights into its root causes and potential solutions.

For example, let’s say you’re facing a recurring issue in your daily commute – traffic congestion. By analyzing the problem, you may discover that the main causes are a lack of alternative routes and a lack of communication between drivers. This analysis helps you identify potential solutions such as using navigation apps to find alternate routes or promoting carpooling to reduce the number of vehicles on the road.

Gathering and Analyzing Data

Once we have identified the problem, it is important to gather relevant data to support our analysis. This may involve conducting surveys, collecting statistics, or reviewing existing research. By gathering data, we can make informed decisions and prioritize potential solutions based on their impact and feasibility.

Continuing with the traffic congestion example, you may gather data on the average commute time, the number of vehicles on the road, and the impact of carpooling on congestion levels. This data can help you analyze the problem more accurately and determine the most effective solutions.

Generating and Evaluating Solutions

After analyzing the problem and gathering data, the next step is to generate potential solutions. This can be done through brainstorming, researching best practices, or seeking input from experts. It is important to consider multiple options and think outside the box to find innovative and effective solutions.

For our traffic congestion problem, potential solutions can include implementing a smart traffic management system that optimizes traffic flow or investing in public transportation to incentivize people to leave their cars at home. By evaluating each solution’s potential impact, cost, and feasibility, you can make an informed decision on the best course of action.

Implementing and Iterating

Once a solution has been chosen, it is time to implement it in real life. This may involve developing a plan, allocating resources, and executing the solution. It is important to monitor the progress and collect feedback to learn from the implementation and make necessary adjustments.

For example, if the chosen solution to address traffic congestion is implementing a smart traffic management system, you would work with engineers and transportation authorities to develop and deploy the system. Regular evaluation and iteration of the system’s performance would ensure that it is effective and making a positive impact on reducing congestion.

By applying the problem-solving cycle derived from computer science to real-life situations, we can approach challenges with a systematic and analytical mindset. This can help us make better decisions, improve our problem-solving skills, and ultimately achieve more efficient and effective solutions.

Building Problem Solving Skills

In the field of computer science, problem-solving is a fundamental skill that is crucial for success. Whether you are a computer scientist, programmer, or student, developing strong problem-solving skills will greatly benefit your work and studies. It allows you to approach challenges with a logical and systematic approach, leading to efficient and effective problem resolution.

The Problem Solving Cycle

Problem-solving in computer science involves a cyclical process known as the problem-solving cycle. This cycle consists of several stages, including problem identification, data analysis, solution development, implementation, and evaluation. By following this cycle, computer scientists are able to tackle complex problems and arrive at optimal solutions.

Importance of Data Analysis

Data analysis is a critical step in the problem-solving cycle. It involves gathering and examining relevant data to gain insights and identify patterns that can inform the development of a solution. Without proper data analysis, computer scientists may overlook important information or make unfounded assumptions, leading to subpar solutions.

To effectively analyze data, computer scientists can employ various techniques such as data visualization, statistical analysis, and machine learning algorithms. These tools enable them to extract meaningful information from large datasets and make informed decisions during the problem-solving process.

Developing Effective Solutions

Developing effective solutions requires creativity, critical thinking, and logical reasoning. Computer scientists must evaluate multiple approaches, consider various factors, and assess the feasibility of different solutions. They should also consider potential limitations and trade-offs to ensure that the chosen solution addresses the problem effectively.

Furthermore, collaboration and communication skills are vital when building problem-solving skills. Computer scientists often work in teams and need to effectively communicate their ideas, propose solutions, and address any challenges that arise during the problem-solving process. Strong interpersonal skills facilitate collaboration and enhance problem-solving outcomes.

• Mastering programming languages and algorithms
• Staying updated with technological advancements in the field
• Practicing problem solving through coding challenges and projects
• Seeking feedback and learning from mistakes
• Continuing to learn and improve problem-solving skills

By following these strategies, individuals can strengthen their problem-solving abilities and become more effective computer scientists or programmers. Problem-solving is an essential skill in computer science and plays a central role in driving innovation and advancing the field.

Questions and answers:

What is the problem solving cycle in computer science.

The problem solving cycle in computer science refers to a systematic approach that programmers use to solve problems. It involves several steps, including problem definition, algorithm design, implementation, testing, and debugging.

How important is the problem solving cycle in computer science?

The problem solving cycle is extremely important in computer science as it allows programmers to effectively tackle complex problems and develop efficient solutions. It helps in organizing the thought process and ensures that the problem is approached in a logical and systematic manner.

What are the steps involved in the problem solving cycle?

The problem solving cycle typically consists of the following steps: problem definition and analysis, algorithm design, implementation, testing, and debugging. These steps are repeated as necessary until a satisfactory solution is achieved.

Can you explain the problem definition and analysis step in the problem solving cycle?

During the problem definition and analysis step, the programmer identifies and thoroughly understands the problem that needs to be solved. This involves analyzing the requirements, constraints, and possible inputs and outputs. It is important to have a clear understanding of the problem before proceeding to the next steps.

Why is testing and debugging an important step in the problem solving cycle?

Testing and debugging are important steps in the problem solving cycle because they ensure that the implemented solution functions as intended and is free from errors. Through testing, the programmer can identify and fix any issues or bugs in the code, thereby improving the quality and reliability of the solution.

What is the problem-solving cycle in computer science?

The problem-solving cycle in computer science refers to the systematic approach that computer scientists use to solve problems. It involves various steps, including problem analysis, algorithm design, coding, testing, and debugging.

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• A Comprehensive Guide to the Problem Solving Cycle in Psychology – Strategies, Techniques, and Applications
• The Step-by-Step Problem Solving Cycle for Effective Solutions
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Computer Science Fundamentals

Free set of elementary curricula that introduces students to the foundational concepts of computer science and challenges them to explore how computing and technology can impact the world.

Free, and fun, elementary courses for each grade

• Six courses, one for each elementary grade
• Equitable introductory CS courses
• Use the same course for all students in the same grade, regardless of their experience
• All courses make suitable entry points for students

Picking the right CS Fundamentals course for your classroom

With the diverse set of options offered for CS Fundamentals, there is a course for all different needs.

How will your students engage with the content?

Courses specifically designed for your elementary classroom.

Find the course for the grade you teach. Each course is approximately a month long.

Kindergarten

Program using commands like loops and events. Teach students to collaborate with others, investigate different problem-solving techniques, persist in the face of challenging tasks, and learn about internet safety.

Through unplugged activities and a variety of puzzles, students will learn the basics of programming, collaboration techniques, investigation and critical thinking skills, persistence in the face of difficulty, and internet safety.

Create programs with sequencing, loops, and events. Investigate problem-solving techniques and develop strategies for building positive communities both online and offline. Create interactive games that students can share.

Review of the concepts found in earlier courses, including loops and events. Afterward, students will develop their understanding of algorithms, nested loops, while loops, conditionals, and more.

Make fun, interactive projects that reinforce learning about online safety. Engage in more complex coding such as nested loops, functions, and conditionals.

Look at how users make choices in the apps they use. Make a variety of Sprite Lab apps that also offer choices for the user. Learn more advanced concepts, including variables and “for” loops.

Self-paced elementary curriculums

Teachers play a critical role in student learning by teaching our unplugged activities and leading whole class discussions, however, we recognize that CS Fundamentals isn't always taught in a traditional classroom setting. We provide two self-paced express courses alongside Courses A-F. These express courses are designed for situations where teachers allow each student to work at their own pace independently.

Grades: K-1

Pre-Reader Express

Learn the basics of drag-and-drop block coding by solving puzzles and creating animated scenes. Make art and simple games to share with friends, family, and teachers.

Grades: 2-5

Learn to create computer programs, develop problem-solving skills, and work through fun challenges! Make games and creative projects to share with friends, family, and teachers.

No devices? We have you covered

Go ahead, cut the cord (for a while)!

CS education does not always need to be in front of a screen and device access shouldn't be a barrier to learning computer science concepts.

Resources that support you every step of the way

Sign up for a Code.org account to get access to materials that will help you teach computer science with confidence. Code.org has extensive resources designed to support educators, even those without prior CS teaching experience.

Lesson Plans

Get step-by-step guidance, learning objectives, and assessment strategies for effective teaching.

Helpful resources include slide decks, activity guides, rubrics, and more — all organized in one place. Each lesson plan is accompanied by tips for classroom implementation, differentiation ideas, and extension activities to cater to students of all abilities.

Instructional Videos

Watch easy-to-understand overviews of computer science and programming concepts.

Code.org video series are designed specifically to support your classroom and are engaging and fun to watch.

Slide Decks

We offer educators an organized, visually engaging, and pedagogically sound framework to deliver computer science lessons.

Code.org slide decks provide step-by-step instructions, examples, and interactive activities that align with curricular objectives.

Assessments

Our curricula includes a comprehensive system of formative and summative assessment resources.

These include rubrics, checklists, mini-projects, end-of-chapter projects, student-facing rubrics, sample projects, and post-project tests — all designed to support teachers in measuring student growth, providing feedback, and evaluating student understanding.

Programming Tools

Code.org's integrated development environments (IDEs) cater to students of all skill levels.

We offer a versatile and user-friendly platform that supports a variety of programming paradigms. This enables learners to seamlessly transition from block-based coding to text-based languages, and fosters creativity and innovation.

Professional learning that meets your needs

Get the support you need as you prepare to teach. Teachers love it, with over 90% ranking it the best professional development ever!

Facilitator-led Workshops

Join local teachers for inspiring and hands-on support to implement computer science in your classroom. Our Regional Partners offer high-quality, one-day Code.org workshops for individual teachers or for schoolwide PD. Sign up for a professional development workshop near you!

Self-Paced Online Modules

Through reading, viewing videos, completing interactive puzzles, and reflecting on your learning, you will develop your own understanding while preparing to teach computer science in your classroom.

Frequently asked questions

CS Fundamentals was written using both the K-12 Framework for Computer Science and the CSTA standards as guidance. Currently, every lesson in CS Fundamentals contains mappings to the relevant CSTA standards. The summary of all CSTA mappings for each course can be found at:

• Course A Standards
• Course B Standards
• Course C Standards
• Course D Standards
• Course E Standards
• Course F Standards

A Google Sheets version of the standards can be found at CSF Standards .

The leading K-12 CS curriculum in the United States, our elementary program has been proven effective in major urban school districts like Dallas, as well as small rural districts in Iowa. There is no need to hire specialists to teach CS. Our program is uniquely designed to support teachers new to CS while offering the flexibility to evolve lessons to fit student needs. Share this brochure with your school and district administrators, or suggest they take a look at our administrators page specially designed to answer administrators' most common questions.

Our curriculum and platform are available at no cost for anyone, anywhere, to teach!

New to teaching computer science? No worries! Most of our teachers have never taught computer science before. Join local teachers for inspiring and hands-on support to implement computer science in your classroom. Our Regional Partners offer high-quality, one-day Code.org workshops for individual teachers or for schoolwide PD. Sign up for a professional development workshop near you !

Join over 100,000 teachers who have participated in our workshops. The majority of our workshop attendees say, 'It's the best professional development I've ever attended.' In fact, 90% of attendees would recommend our program to other teachers !

Each CSF course includes 13-17 lessons designed for 45-minute periods. We recommend all students move from lesson to lesson at a pace set by the teacher. There are many teacher-led project levels designed to be experienced in unison while the skill-building lessons can be completed by students at their own pace.

Many lessons have handouts that guide students through activities. These resources can be printed or assigned digitally. Some lessons call for typical classroom supplies and manipulatives. Visit the CSF Syllabus to learn more .

Support and questions

Still have questions? Reach out to us! We are here to help.

Our support team is here to answer any questions you may have about starting teaching with Code.org. You can also ask other teachers about their experience on our teacher forums.

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• Exploring Computational Thinking

As part of our ongoing partnership with the broader educational community, we are releasing the Google Exploring Computational Thinking resources (including the Computational Thinking for Educators online course) to several practitioner organizations working to support CT teaching and learning globally. The resources, including the curated collection of lesson plans, videos, and other resources were created to provide a better understanding of CT for educators and administrators, and to support those who want to integrate CT into their own classroom content, teaching practice, and learning. We encourage you to access all these resources at:

International Society for Technology in Education (ISTE)

• ISTE U – Introduction to Computational Thinking for Every Educator
• Exploring Computational Thinking resource repository

Australian Digital Technologies Hub

• Lesson ideas mapped to the Australian Digital Technologies curriculum, based on the original resources developed as Exploring Computational at Google.

CT Overview

Computational Thinking (CT) is a problem solving process that includes a number of characteristics and dispositions. CT is essential to the development of computer applications, but it can also be used to support problem solving across all disciplines, including math, science, and the humanities. Students who learn CT across the curriculum can begin to see a relationship between subjects as well as between school and life outside of the classroom.

CT involves a number of skills, including:

• Formulating problems in a way that enables us to use a computer and other tools to help solve them
• Logically organizing and analyzing data
• Representing data through abstractions such as models and simulations
• Automating solutions through algorithmic thinking (a series of ordered steps)
• Identifying, analyzing, and implementing possible solutions with the goal of achieving the most efficient and effective combination of steps and resources
• Generalizing and transferring this problem solving process to a wide variety of problems

These skills are supported and enhanced by a number of dispositions or attitudes that include:

• Confidence in dealing with complexity
• Persistence in working with difficult problems
• Tolerance for ambiguity
• The ability to deal with open ended problems
• The ability to communicate and work with others to achieve a common goal or solution

CT concepts are the mental processes (e.g. abstraction, algorithm design, decomposition, pattern recognition, etc) and tangible outcomes (e.g. automation, data representation, pattern generalization, etc) associated with solving problems in computing. These include and are defined as follows:

• Abstraction: Identifying and extracting relevant information to define main idea(s)
• Algorithm Design: Creating an ordered series of instructions for solving similar problems or for doing a task
• Automation: Having computers or machines do repetitive tasks
• Data Analysis: Making sense of data by finding patterns or developing insights
• Data Collection: Gathering information
• Data Representation: Depicting and organizing data in appropriate graphs, charts, words, or images
• Decomposition: Breaking down data, processes, or problems into smaller, manageable parts
• Parallelization: Simultaneous processing of smaller tasks from a larger task to more efficiently reach a common goal
• Pattern Generalization: Creating models, rules, principles, or theories of observed patterns to test predicted outcomes
• Pattern Recognition: Observing patterns, trends, and regularities in data
• Simulation: Developing a model to imitate real-world processes

See our Computational Thinking Concepts Guide for a printable version of this list, along with teaching tips for each concept.

CT Materials

Incorporate computational thinking (CT) into your curriculum with these classroom-ready lesson plans, demonstrations, and programs (available in Python and Pencil Code ). All materials in this collection have been aligned to both core subject* and CS** education standards. For more information on the connections between the CS education standards, see our International CS Education Standards crosswalk .

* See Common Core State Standards and Next Generation Science Standards ** See CSTA K–12 Computer Science Standards (United States), CAS: Primary School and Secondary School (United Kingdom), Australia , New Zealand , and Israel

Core Subject: All

Subject: All

Suggested Age: 8-18

Type: Reference

Computational Thinking Concepts Guide

This guide explores eleven terms and definitions for Computational Thinking (CT) concepts, enabling you to incorporate them into existing lesson plans, projects, and demonstrations. Teaching tips are included for each concept.

Differentiation Strategies Guide

This guide contains codes for seven differentiation strategies and their meanings. Differentiation strategies are practices for modifying content or instructional practices for a specific group of students.

Student Engagement Strategies Guide

This guide describes ten strategies for capturing and maintaining student attention during classroom lessons. These student engagement strategies can be interspersed throughout existing lesson plans, projects and activities to increase student interest in any topic.

Pseudocode Guide

This guide explores the benefits of using pseudocode, an informal, high-level description of the operating procedure of a computer program or other algorithm. With pseudocode, students can learn how plan out their programs even if they do not have access to a computer.

Introduction to Python

This guide to the Python programming languages helps you explore sample topics including mathematical notation, testing for equality, writing Python programs, and conditional logic.

Python Basics Quick Reference

This handy reference to programming in Python contains the most frequently used functions and syntax from the Exploring Computational Thinking lesson plans.

Core Subject: Computer Science

Subject: Algorithms and Complexity

Suggested Age: 14-18

Type: Lesson

Measuring the Complexity of a Function or Algorithm

This lesson plan explores problems that are easy for the computer to solve and problems that are difficult for the computer to solve. Students will learn how to measure the complexity of a function/algorithm and how this applies to real world situations.

Suggested Age: 8-12

Ciphering a Sentence

This lesson plan enables student to develop a cipher, encode a sentence, and then develop an algorithm for encoding and decoding.

Suggested Age: 11-18

Algorithmic Thinking

This lesson plan demonstrates that an algorithm is a precise, step-by-step set of instructions. Students will be asked to create oral algorithms to solve problems that other students can then use effectively.

Suggested Age: 11-14

Divide and Conquer

This lesson plan requires students to use a ‘divide-and-conquer’ strategy to solve the mystery of the “stolen crystals”. Students will use decomposition to break the problem into smaller problems and algorithmic design to plan a solution strategy.

Water Water Everywhere!

This lesson plan presents students with the challenging problem of measuring a volume of water using containers of the wrong measurement size. Students will decompose a complex problem into discrete steps, design an algorithm for solving the problem, and evaluate the solution efficiencies and optimization in a simulation.

Data Compression

This lesson introduces students to the need for data compression and methods for reducing the amount of data in both text and images by applying a filter. By looking for patterns and adjusting the algorithm based on the results, students will learn to reduce the memory size with minimal impact on the quality.

Subject: Data Analysis

Suggested Age: 8-15

Describing an Everyday Object

This lesson plan explores the difficulty of providing detailed descriptions of objects without using their names. The CT concepts covered include abstraction, data representation and pattern recognition.

Exploring Your Environment

This lesson plan enables students to gather data about a place or environment, organize that data in a table, and look for patterns. The CT concepts covered include data collection, data representation, data analysis, and decomposition.

Subject: Logic

Suggested Age: 9-12

Machine Testing

This lesson plan presents students with a mysterious new machine and requires them to develop testing strategies to determine its functionality.

Solving a Guessing Game with Data

This lesson plan requires students to develop two guessing games. The CT concepts covered include data collection, data representation, data analysis, and algorithm design.

Subject: Software Development

Suggested Age: 13-18

Functions and Algorithms

This lesson plan enables students to identify, evaluate, follow, and create functions, including functions that loop, functions that include decisions, and functions that include both. The activities increase in difficulty and students should continue as far as they are able to.

Core Subject: English-Language Arts

Subject: Language

Indefinite Articles

This lesson plan explores the usage of ‘a’ and ‘an’. Students will use pattern recognition and pattern generalization to determine when to use these indefintite articles and then develop a written algorithm that enables them to refine basic algorithms to handle exceptions to a generalized rule.

Suggested Age: 8-10

Mystery Word X

This lesson plan enables students to analyze the classification of nouns and verbs. They begin by considering nouns as “a person, place, or thing” and verbs as “action words. They then run a group of words through a series of "tests" and identify instances in which this standard notion might lead to errors.

Present Participle

This lesson plan enables students to investigate how the ending letters of a verb affect its spelling as tense changes. Students begin by simply adding ‘ing’ to the end of verbs. By identifying patterns in the spelling of verbs for which this works and those for which it does not, students build a stronger algorithm for conjugating verbs.

Finding Patterns in Spelling Errors and History

This lesson plan helps students learn how to analyze spelling errors and large data sets to find patterns, develop abstractions, and discover how large amounts of data can reveal much about our society.

Suggested Age: 10-14

Writing a Story

This lesson plan enables student to collaborate with others to build a story, identify any "bugs" in the story, and fix those bugs to give the story a more logical flow.

Type: Program

Interactive Fiction

This Pencil Code program enables students to create a simple piece of interactive fiction with three "pages", with one function representing each page, and buttons to select the next action. Students can analyze, fill in, or change parts of the program.

Interactive Mad Libs

This Pencil Code program creates an interactive Mad Libs game, prompting the user to enter several words matching requested parts of speech and then stitching them together in humorous sentences. Students can analyze, fill in, or change parts of the program.

Interactive Mad Libs (Variation)

This Pencil Code program is a variation on the interactive Mad Libs program that automatically generates sentences by randomly choosing words. Students can analyze, fill in, or change parts of the program.

Lady Macbeth Chat Bot

This Pencil Code program enables students to create an interactive chat bot that answers questions as if it were Lady Macbeth. Students can students analyze, fill in, or change parts of the program.

Stroke Order of a Chinese Character

This Pencil Code program enables students to illustrate the stroke order of a chinese character by creating their own rendering of a Chinese character and drawing the strokes in the right order. Students can analyze, fill in, or change parts of the program.

Type: Exploration

This exploration gives students algorithms they can modify to improve the virtual Countess Ada Lovelace's ability to respond to questions.

Core Subject: History Social Science

Subject: US History

Map Visualization

This Pencil Code program provides a simple way to illustrate statistics geographically by drawing bubbles on a map. Students can analyze, fill in, or change parts of the program.

Population Statistics

This Pencil Code program enables student to create a population graph from data in a spreadsheet. Students can analyze, fill in, or change parts of the program.

Core Subject: Mathematics

Subject: Algebra

Suggested Age: 12-15

Linear Association

This lesson plan uses CT concepts to explore the linear association between variables using two sets of data. Students will read data in a spreadsheet and in a graph and identify positive and negative linear association based on the shape of the graph.

Degrees and Radians

This lesson plan uses basic patterns to label key points on the unit circle in terms of degrees, and then follows a similar process to relabel these points in terms of radians. Students can then develop an algorith to convert between degrees and radians based on the patterns they used to count their way around the unit circle.

Slope and Y-Intercept

This lesson plan uses CT to explain the properties of slope and y-intercept. Students will learn how to calculate the slope and y-intercepts of a line that passes through a given set of points, and then use Python to solve various challenging slope and y-intercept exercises.

Suggested Age: 13-16

Two Workers

This Python program helps students solve word problems with two people working together at different rates. Students can analyze, fill in parts of, or enhance the program to solve more sophisticated work problems.

Three Workers

This Python program helps students solve word problems with three people working together at different rates. Students can analyze, fill in parts of, or enhance the program to solve more sophisticated problems.

Savings and Interest

This Python program helps students understand how to calculate interest based on the savings amount, interest rate, and number of years of investing. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

This Python program helps students conceptualize the following word problem: Charisse is buying two different types of cereals from the bulk bins at the store. Granola costs $2.29 per pound, and muesli costs $3.75 per pound. She has $7.00. Use x as the amount of granola and y as the amount of muesli. How many pounds of granola can she buy if she buys 1.5 pounds of muesli?

DVD Rentals

This Python program helps students conceptualize the following word problem: Shanti has just joined a DVD rental club. She pays a monthly membership fee of $4.95, and each DVD rental is $1.95. If Shanti’s budget for DVD rentals in a month is $42, how many DVDs can Shanti rent in her first month if she doesn’t want to go over her budget?

Theme Park Ride

This Python program helps students conceptualize the following word problem: There are 90 people in line at a theme park ride. Every 5 minutes, 40 people get on the ride and 63 join the line. Estimate how long it would take for 600 people to be in line.

T Tables for Simple Functions

This Python program helps students compute the T table for a given function. Students can analyze, fill in parts of, or use the program to check solutions to exercises on which they are already working.

Suggested Age: 12-16

This Python program helps students understand ratios by solving for x in the equation a/b = c/d, where x can be in any location in the two fractions. Students can analyze, fill in parts of, or use the program to check solutions to exercises on which they are already working.

Quadratic Formula

This Python program helps students automatically compute the quadratic formula given the values of a, b and c. Students can analyze, fill in parts of, or use the program to check solutions to exercises on which they are already working.

This Python program helps students use their knowledge of FOIL on zero-variable or one-variable expressions to automatically solve various expressions. Students can analyze, fill in parts of, or use the program to check solutions to exercises on which they are already working.

Factoring Perfect Square Binomial Expressions

This Python program helps students factor binomial expressions into the form (x+c)^2 if the expression fits the pattern. Students can analyze, fill in parts of, or use the program to check solutions to exercises on which they are already working.

Distance, Rate, Time

This Python program helps students automatically compute distance, rate, or time, given two of the three variables. Students can analyze, fill in parts of, or use the program to check solutions to exercises on which they are already working.

Binomial Products

This Python program helps students automatically calculate the binomial product, that is, (ax + b)(cx + d) = acx^2 + adx + bcx + bd. Students can analyze, fill in parts of, or use the program to check solutions to exercises on which they are already working.

This Python program helps students see the connection between a mathematical function and a programmatic function by defining a function in Python and seeing what it means to pass a value to that function.

Properties of Quadratic Equations

This Python program helps students apply their knowledge of quadratic equations to automatically complete the square of a quadratic equation and find the location of the vertex. Students can analyze, fill in parts of, or use the program to check solutions to exercises on which they are already working.

Substitution with Two Equations

This Python program enables students to substitute and solve for variables using two equations. The first equation can be any equation; the second must be of the form variable = ... where variable appears in the first equation. Students can analyze the program or predict the substitution given the two equations.

Pascal’s Triangle

This Python program illustrates how Pascal’s Triangle is computed. Students can trace through the program and learn more about nested for-loops and why they are needed in certain applications. This program may require additional guidance from the educator.

Vertex of a Quadratic

This Python program anables students to calculate the vertex for any given quadratic and automatically calculate the vertex (h, k) for a given quadratic in the form of y = ax^2 + bx + c. Students can analyze or fill in parts of the program to reinforce their understanding.

Roots of an Equation

This Python program enables students to solve for the roots of an equation. Students can analyze or fill in parts of the program to reinforce their knowledge.

Conic Sections

This Python program illustrates how the coefficients of functions representing conic sections can be used to determine the type of conic section (circle, ellipse, hyperbola) and display results based on that conic section. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Combinations: n choose k

This Python program enables students to check solutions to combinations (n choose k) exercises. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Matrix Multiplication

This Python program helps students develop their understandings of matrix multiplication by performing it on two randomly generated matrices. Students can analyze or fill in parts of the program to reinforce their understanding. This program is fairly sophisticated and may only work for students with prior Python experience.

Logarithm Notation

This Python program helps students develop their understanding of logarithm notation by automatically computing the result of a given base and exponent and displaying it in log notation. Students can analyze or fill in parts of the program to reinforce their knowledge.

Determinant of a 3x3 Matrix

This Python program enables students to find the determinant of a 3x3 matrix. Students can analyze or fill in parts of the program to reinforce their knowledge.

Determinant of a 2x2 Matrix

This Python program enables students to find the determinant of a 2x2 matrix. Students can analyze or fill in parts of the program to reinforce their knowledge.

Subject: Arithmetic

This Pencil Code program enables student to play the "chaos game" by randomly moving the turtle to create a pattern. Students can analyze, fill in, or change parts of the program.

Graphing Sums of Dice Rolls

This Pencil Code program illustrates randomness by rolling two dice 100 times and graphing the results in two different ways.

Random Number Illustrator

This Pencil Code program can be used to generate and then illustrate a random number. Students can analyze, fill in, or change parts of the program.

Sum of Two Dice

This Pencil Code program can be used to roll two dice a number of times and then print the sum. Students can analyze, fill in, or change parts of the program.

Subject: Calculus

Suggested Age: 16-18

Instantaneous Rate of Change

This Python program enables students to determine the instantaneous rate of change for a given function and then automatically calculate it for a given function. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Calculating Definite Integrals

This Python program enables students to calculate the definite integral for a given function and then automatically calculate it for a specified function. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Fundamental Theorem of Calculus

This Python program enables students to use the Fundamental Theorem of Calculus for a given function and automatically calculate it for a specified function. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Mean and Standard Deviation

This lesson plan demonstrates how to use standard deviation to better understand a set of data. Students will use standard deviation to determine the general pattern/shape of a given set of data to draw more reliable conclusions.

Application and Modeling of Standard Deviation

This lesson plan explores using the central tendency to discover patterns in data. Students will simulate a dice-throwing game and alter the algorithm design to reflect changes to the game. The CT concepts covered include data collection, decomposition, abstraction, and data analysis.

Using Data from Sensors - Introduction

In this lesson plan, students identify and describe various sensors. Students will use sensors to collect data and use Computational Thinking to decompose one large problem into multiple smaller problems.

Using Data from Sensors - Filters and Functions

In this lesson plan, student explore the use of filters to isolate and analyze data generated by various types of sensors. Students use computational thinking to identify patterns generated by a potential agent during a specific activity (such as a human falling to the ground).

Continuous vs Discrete Data - Introduction

This lesson plan illustrates how data can be continuous or discrete. Students will collect data from classmates and then use data analysis and data representation to label the data as continuous or discrete. They will also learn to recognize different graphical and tabular representations of data as discrete and continuous.

Continuous vs Discrete Data - Modeling Continuous Functions

This lesson plan requires students to apply their knowledge about continuous and discrete data to categorize data from historical calculations of the speed of light and to examine the effects of modeling a continuous curved shape with an increasing number of discrete points and segments.

Subject: Geometry

Turtle Geometry

This exploration provides students an opportunity to understand the relationship between the number of sides in a regular polygon and its angles. Students will draw shapes using simple commands like 'turn right 90 degrees' and 'move forward 100 steps' and use the patterns they find to write an algorithm for drawing any regular polygon.

Suggested Age: 13-17

Area of a Circle

This lesson plan uses CT to explain the derivation of the formula A = pi*r^2. Students will complete Python programs that calculate the area of a circle as well as individual sectors.

Finding the Shortest Path

This lesson invites students to develop a process for traveling across the country in the most efficient way possible. Students will refine their process after experimenting with smaller networks of points as well as a varient of the Traveling Salesperson problem.

Suggested Age: 11-16

Pythagorean Theorem - Pencil Code

This Pencil Code program enables students to use the Pythagorean Theorem to calculate a third side of a right triangle given the other two sides. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Acute, Obtuse, and Right Triangles

This Python program helps students precisely define the relationships between the angles for different types of triangles (acute, obtuse, or right). Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Calculating Surface Area

This Python program helps students use surface area formulas to automatically to calculate the surface areas of several geometric objects (cube, rectangular prism, cylinder, sphere). Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Pythagorean Theorem - Python

This Python program helps students use the Pythagorean Theorem to calculate a third side of a right triangle given the other two sides. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Polygonal Formulas

This Python program helps students use formulas related to polygons to display several results based on the number of sides of a polygon. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Distance Between Two Points

This Python program helps students use the distance formula to automatically calculate the distance between two points (x1, y1) and (x2, y2). Students can analyze or fill in parts of the program to reinforce their understanding.

Area Calculations

This Python program demonstrates how area formulas can be used to automatically compute the area of various geometric objects. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Suggested Age: 12-14

This lesson plan requires students to apply logical reasoning to deduce information from rules in a game scenario. The CT concepts covered include data representation, data analysis, and decomposition.

Pattern Machine

This lesson plan requires students to play a triplet game in which a set of three numbers can be described according to a specific rule. Students use data analysis to recognize and generalize patterns from which they derive the rule and solve the puzzle.

This lesson plan requires student to use logical reasoning to deduce information about the labels on fruit boxes based upon rules. The CT concepts covered include data analysis and simmulation.

Suggested Age: 10-12

Logic Party

This lesson plan requires students to solve a numerical problem using constraints to graphically eliminate possibilities and arrive at the correct answer. The CT concepts covered include data representation, data analysis, and decomposition.

Subject: Pre-Algebra

Fraction Addition and Common Denominators

This lesson plan explores how to find a common denominator between two fractions and add or subtract the fractions. It covers a variety of CT concepts, including decomposition, abstraction, pattern recognition, pattern generalization and algorithm design.

Multiplication with Fractions

This lesson plan explores how to visualize the multiplication of fractions and identify patterns between the multiplicands and their product. Upon completion of this lesson, students will be able to multiply simple fractions using a visual model and a computational algorithm.

Suggested Age: 11-13

Ratios and Proportions

This lesslon plan uses CT concepts and the Python programming language to develop an algorithm for answering questions involving ratios and proportions. It coveres a variety of CT concepts including problem decompostion, abstraction, pattern identification, pattern generalization and algorithm design.

Multiplying by Numbers Between Zero and One

This lesson plan uses CT concepts to to demonstrate that when multiplying a positive number by a decimal between 0 and 1, the product is always less than the original number.

Dividing by Numbers Between Zero and One

This lesson plan uses CT concepts to demonstrate that when dividing a positive number by a decimal between 0 and 1, the quotient is always greater than the original number.

Common Fractions and Equivalent Percentages

This lesson plan uses CT concepts to demonstrate the conversion of common fractions into their equivalent percentages. Students identify patterns between fractions, decimals, and percents, and generalize these patterns.

Percent Change

This lesson plan uses CT concepts to demonstrate how to calculate the percent change between any two numbers. Students identify patterns in percent change and decompose an algorithm to help strengthen their understanding.

Scientific Notation

This lesson plan uses CT concepts to identify patterns between the exponent, the number of places the decimal point moves, and the direction the decimal point moves when multiplying by powers of ten.

Percentages

This lesson plan uses CT concepts to demonstrate how to develop an algorithm for calculating percentages using mental math.

Long Multiplication on Two-Digit Numbers - Pencil Code

This Pencil Code program enables student to perform long multiplication on two-digit numbers, for example, 42 x 31. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Long Multiplication on Two-Digit Numbers - Python

This Python program enables students to perform long multiplication on two-digit numbers, for example, 23 x 46. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Fractions and Proportions

This Python program enables students to check whether two fractions are proportional. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Lemonade and Glasses

This Python program helps students conceptualize word problems, specifically: Sam has a jar with 5 cups of fresh lemonade. Jack has some glasses which hold 1.5 cups each of liquid. How many glasses of lemonade can Jack serve of Sam’s lemonade?

Evaluating Expressions

This Python program llustrates how a basic calculator functions. It introduces Python’s eval function as a way of computing expressions containing variables a, b, and c when given values for each of these variables. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Midpoint Between Two Points

This Python program helps students apply their knowledge of the midpoint formula to automatically calculate the midpoint between two points (x1, y1) and (x2, y2). Students can analyze or fill in parts of the program to help reinforce their understanding.

Complementary and Supplementary Angles

This Python program helps students apply their knowledge of complements and supplements to automatically compute the complement and supplement of a given angle. Students can analyze or fill in parts of the program to help reinforce their understanding.

Populations

This Python program helps students determine how long it will take to reach a certain target population, given a starting population, birthrate, and death rate. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Rock Climber, Cliff, and Shadows

This Python program helps students conceptualize the following word problems: A rock climber wants to know the height of a cliff. She measures the shadow of her friend, who is 5 feet tall and standing beside the cliff and measures the shadow of the cliff. If the friends shadow is 4 feet long and the cliffs shadow is 60 feet long, how tall is the cliff?

Basketball Hoops and Buildings

This Python program helps students conceptualize the following word problem: A basketball rim 10 ft high casts a shadow 15 ft long. At the same time, a nearby building casts a shadow that is 54 ft long. How tall is the building?

Fractional Exponents

This Python program demonstrates fractional exponents by automatically computing one based on a given base and fractional exponent. Students can analyze or fill in parts of the program to reinforce their knowledge.

Subject: Statistics and Probability

Combinations with Repeats

This lesson plan uses CT concepts to illustrate how to compute the number of possible arrangements for a given number of digits in a given number of spaces. Students will identify patterns in relatively easy cases that can lead them to an algorithm which applies to all cases.

Factorials with Names

This lesson plan uses CT concepts to investigate the number of possible arrangements of the letters in a given name. Students will identify patterns in the number of possible arrangements given an increasing number of letters, and then decompose the results to arrive at the factorial function.

Sorting Data

This lesson plan illustrates how to sort data using spreadsheet functions and/or Python. Students compare the algorithms used by both tools and then write their own algorithms for analyzing data with the mean, median, and mode.

Surveys and Estimating Large Quantities

This lesson plan shows students how to estimate the approximate size of data and determine the extent to which that data is realiable. Students will observe smaller data sets and identify patterns that enable them to make general predictions and to create algorithms capable of making approximations.

Randomness in Stochastic Models

This lesson plan explores random variables and probability. In this lesson, students will be introduced to methods to create random numbers as well as ways in which randomization can be used in scientific experiments.

Stochastic and Deterministic Modeling

This lesson plan explores deterministic models (the output is always the same) and stochastic models (the output is based on random sampling and can vary) and how, by modeling real phenomena using simulations, it is possible to improve a model and make better predictions.

Analyzing Discrete and Continuous Data in a Spreadsheet

In this lesson plan, students will collect data in a spreadsheet and learn to use various functions and analysis tools to better see patterns in their eating habits.

Analyzing Discrete and Continuous Data in a Map

This lesson plan illustrates how data is more than just numbers and that a map can also be a source of both discrete and continuous data. Using various tools, students will analyze and calculate the amount of urban open space available in their city.

Correlation vs Causation

In this lesson, plan, students will test the strength of a correlation and discern whether or not a law or conclusion can be made based on that correlation. Students will see the threshold commonly accepted for correlating data and test their own assumptions about causation.

Data Aggregation and Decomposition (Advanced Python)

This lesson plan explores how to use/analyze data to draw conclusions about the world around us. Students will improve their computational thinking by collecting/aggregating data onto a spreadsheet, identifying patterns in their data, decomposing the data into specified groups for analysis and further pattern recognition, and modifying an algorithm written in Python to facilitate data analysis.

Data Aggregation and Decomposition (Google Sheets)

This lesson plan uses CT to help students decompose and re-aggregate small sets of data using Google Sheets. Students use decomposition to break down long lists of information and write basic algorithms to use for the data analysis process.

The Law of Large Numbers and Probability

This lesson plan uses CT to help students use large amounts of data to explore the Law of Large Numbers and the Birthday Paradox to see how closely projected calculations match outcomes in the real world.

Generating Complex Behavior with Algorithms

This lesson plan provides examples of complex behavior that students can explore such as flipping a coin and cellular automata. Students can modify the algorithms to see the impact it has on the behavior.

Subject: Trigonometry

Suggested Age: 12-17

Application of Sin(x) and Cos(x)

This Python program enables students to graph two functions and apply their knowledge of the fact that C*sin(x + p) is the same as A*sin(x) + B*cos(x), for the right choice of A and B. Students can analyze, fill in parts of, or use the program to check results to exercises on which they are already working.

Core Subject: Music

Subject: Music

Making Music with Algorithms

This lesson plan allows students to examine the various aspects of music such as scales, melody, and rhythm. The patterns they discover will enable them to modify an algorithm to improve the quality of the music generated by the algorithm.

Core Subject: Science

Subject: Biology

Modeling the Genome using Computational Thinking

This demonstration explores how scientific knowledge of DNA progressed over the course of sixty years to the point where scientists could encode genes using a computer. It covers a variety of CT concepts, including decomposition, pattern recognition, abstraction, and algorithm design and their relation to natural phenomena.

Modeling GDP and Waste using Computational Thinking

This demonstration explores the hazards of making decisions based on incomplete data. It covers a variety of CT concepts, including decomposition, pattern recognition, abstraction, and algorithm design and their relation to natural phenomena.

Modeling Natural Selection using Computational Thinking

This demonstration illustrates how Charles Darwin and Gregor Mendel use Computational Thinking methods to make foundational discoveries in natural selection. It covers a variety of CT concepts, including decomposition, pattern recognition, abstraction, and algorithm design and their relation to natural phenomena.

Suggested Age: 14-17

Cell Biology - Filters

This lesson plan uses CT to improve students' understandings of filters in cell bioloigy. Students will find patterns in filters of all types to help them understand how these filters function. Prior to this lesson, have students complete the related lesson titled Inquiry and Observation.

Cell Biology - Filter Design and Construction

This lesson plan uses Computational Thinking to help students understand the movement of molecules across a cell membrane. Students will decompose their “molecules” to develop a design for their own “cell membranes” and then write an algorithm to describe them before building them. Prior to this lesson, have students complete the related lesson titled Filters.

Classifying Objects with Computational Thinking

This exploration uses the game '20 Questions' to have students estimate the number of questions necessary to guess any species on Earth. Students will first examine a few smaller classification examples using only 'yes' and 'no' questions, and then will generalize these patterns to develop an equation for classifying any object.

Subject: Chemistry

Modeling Electron Configuration using Computational Thinking

This demonstration uses Computational Thinking to show the relationship between electron configuration and an element’s position in the periodic table. It covers a variety of CT concepts, including decomposition, pattern recognition, abstraction and algorithm design to show how the atomic number of an element affects the configuration of its electrons.

Modeling Radioactive Decay using Computational Thinking

This demonstration explores how Computational Thinking is used to model the radioactive decay of an element. It covers a variety of CT concepts, including decomposition, pattern recognition, abstraction, and algorithm design and their relation to natural phenomena.

Modeling Boyle's Law using Computational Thinking

This demonstration describes how Computational Thinking can be used to understand the relationship between pressure and volume in a container of gas as described by Boyle’s Law. It covers a variety of CT concepts, including decomposition, pattern recognition, abstraction, and algorithm design and their relation to natural phenomena.

Patterns in the Periodic Table

This lesson plan illustrates how spreadsheet functions can be used to identify organizational patterns in the periodic table. The spreadsheet functions presented can be used on any data set.

Sorting the World's Cities (Google Sheets)

This lesson plan demonstrates how to use spreadsheet functions to sort and graph data. Once the data is sorted, students can begin to identify patterns and trends.

Sorting the World's Cities (Advanced Python)

This lesson plan demonstrateshow to read data from a spreadsheet into a Python program and then sort that data. When taught in conjunction with Sorting the World's Cities with Excel, this lesson can help student make the connection between writing a program and using a spreadsheet application.

What is Data? - Introduction

This lesson plan describes what data is, how prevalent it is, and how it can be used to make informed decisions. The CT concepts covered include pattern recognition and data representation.

What is Data? - Code Breaking and Patterns

This lesson plan introduces the concept of data. Students will create new data, look for patterns in existing data and attempt to decode text and numeric messages. They will use data analysis, including pattern recognition, to make sense of the provided data.

This Python program enables students to process data sets using a simple sorting algorithm. It can also be used to illustrate how sorting might be done automatically by an application such as Excel.

Subject: Earth Science

Energy Analysis

This lesson plan explores how spreadsheet functions can be used to analyze data on energy production and consumption around the world. Students learn how to display the results of their data collection on a map of the world, creating a visual representation of the numbers they input into their spreadsheets. This example is most suitable for high school biology or earth science classes.

Subject: Physics

Modeling Projectile Motion using Computational Thinking

In this demonstration illustrates how a program can be used to simulate projectile motion. It enables students to see how decomposition, pattern recognition and abstraction can be used to understand natural phenomena.

Modeling Pendulums using Computational Thinking

This demonstration illustrates how Computational Thinking concepts can be used to explore the laws that govern a pendulum’s motion. It covers a variety of CT concepts, including decomposition, pattern recognition, abstraction, and algorithm design and their relation to natural phenomena.

Modeling Free Fall using Computational Thinking

This demonstration explores how Galileo used Computational Thinking and inclined planes to calculate acceleration of a sphere in free fall. It covers a variety of CT concepts, including decomposition, pattern recognition, abstraction, and algorithm design and their relation to natural phenomena.

Working with Large Tables of Data

This lesson plan enables students to work with large tables of GPS data. Students will learn to sort, manipulate, and visualize data so it can be easily understood.

Simulating a Bouncing Ball

This exploration breaks down the components of motion so students can understand and improve an algorithm for making a ball bounce.

Below is a list of resources on computational thinking (CT). This list is not meant to be comprehensive, but is instead a curated collection of resources that educators and administrators might find useful. For additional computer science and CT resources, try our CS Custom Search .

For educators

General CT Resources

• Computational Thinking for Educators - Online course for learning what CT is and how it can be integrated into a variety of subject areas by exploring examples of CT in your subject area, experimenting with examples of CT-integrated activities, and creating a plan to incorporate it into your classroom
• Computational Thinking - by Jeannette M. Wing (Communications of the ACM)
• Bringing Computational Thinking to K-12 - by Valerie Barr and Chris Stephenson (ACM Inroads)
• Computational Thinking Teacher Resources - provided by ISTE and CSTA
• Computational Thinking with Scratch - provided byHGSE, EDC, and MIT
• Introduction to Computational Thinking - provided by Bitesize BBC
• Computational Fairy Tales (books) by Jeremy Kubica

CT Tips and Strategies

• Computational Thinking Concepts Guide - Comprehensive list of the CT concepts noted on ECT, including tips on implementing each concept in the classroom
• Student Engagement Strategies Guide - Research-based strategies for engaging students
• Differentiation Strategies Guide - Strategies for differentiating instruction in your classroom, based on the groups defined in the Next Generation Science Standards

CT in Computer Science

• CS First - Free, easy-to-use materials based on Scratch that are themed to attract students with varied interests
• CS Unplugged - Free resources and learning activities that teach the principles of Computer Science
• Bebras Challenge : Anytime computing challenges and tasks to introduce students to computational and logical thinking
• Alice - Block-based programming language for creating animations, games, or videos using object-oriented programming constructs in a 3D environment
• App Inventor - Block-based programming language for creating mobile apps for Android
• Pencil Code - Block- and text-based programming environment for creating art, music, games, and stories
• Scratch - Block-based programming language for creating interactive stories, animations, games, music, and art
• Desmos and Geogebra - Two free tools for exploring patterns in math
• Mathalicious - Meaningful and relevant math content with examples of how math is used to solve intriguing questions from a variety of subjects
• Project Euler - Mathematical challenges that require CT to solve them
• Bootstrap - Curriculum that teaches math through computer programming
• CS in Algebra - Partnership between Code.org and Bootstrap which teaches algebraic and geometric concepts through computer programming

CT in Science

• Netlogo - Block-based, multi-agent programmable modeling environment
• CS in Middle School Science - Collection of modules and lessons that augment traditional science instruction with CT through engaging modeling and simulation activities
• PhET Interactive Simulations - Library of interactive, research-based science simulations of physical phenomena that encourage quantitative exploration
• Project GUTS (Growing up Thinking Scientifically) Curriculum - Collection of middle school science units integrating CT
• Wolfram Alpha - Computational knowledge engine for computing answers to queries using facts rather than providing the users with a list of documents or websites

CT in English/Language Arts

• Google Ngram Viewer - Discover patterns and trends in literary works over the last two centuries

CT in Art, Design, Media

• Processing - Programming language and environment for creating programs that are visual and interactive
• Pixly - Block-based programming language for exploring media computation (pixel manipulation of images)

CT in Music

• EarSketch - Computational music remixing and sharing development environment with complementary curriculum

For administrators

CT for School Leaders

• ISTE Computational Thinking Leadership Toolkit

CT in the Science Classroom

• Science and Engineering Practices in the NGSS - See “Practice 5 Using Mathematics and Computational Thinking”

Computer Science Education Standards

• International CS Education Standards crosswalk
• Computer Science Teachers Association (CSTA) - United States
• Computing at School (CAS): Primary School and Secondary School - United Kingdom
• New Zealand

Why is Python the programming language used in the CT materials?

Python is one of the easier languages to start with that is free and easy to download. It offers users two modes: the interpreter mode and the editor mode. See Introduction to Python for general information on how to introduce and use Python in your curriculum, or visit http://www.python.org/ for general Python information.

Some of the Python programs seem too advanced for my students. How can I adapt the materials to work for my particular students?

In developing our exemplar lessons and examples, we wanted to illustrate the various techniques used in computational thinking, from decomposition to algorithm design and implementation. However, we agree that not all the programming exercises are suitable for all students. Thus we really encourage you to adapt our materials to suit the needs of your classroom, which may be dependent on the computing resources you have available as well as the grade and skill level of your students. Below are some ways in which you may choose to adapt our materials:

• Have students complete all of the exercises that lead up to the programs, and have them explain how they would design such an algorithm in their own words instead of writing actual Python programs
• Expose students to the programs by projecting them, analyzing them step-by-step as a class, and then running them using values and variables provided by your students
• Remove logical code sections from the completed programs and have students work together to fill in the missing parts
• Have students work together to enhance a completed program to solve more sophisticated problems that involve different scenarios

How do I install Python on my computer?

Visit http://www.python.org/ for information on how to download and install Python to your computer. Alternatively, if you are unable or do not want to download Python to your computer, you can search online for ‘online Python editor’ to explore the different web-based Python editors.

• About Google

CS for CA News & Updates

Computer science skills: computational thinking explained.

It’s a common misconception that computer science (CS) is only applicable to people working in a technology or STEM careers. However, skills learnt through CS are used in our everyday lives, and in a variety of subjects.

One of these skills is known as computational thinking (CT). 

What is computational thinking?

There are many problem-solving skills involved in computer science, including those needed to design, develop, and debug software. Computational thinking is a way of describing these skills.

Computational thinking refers to the thought processes involved in defining a problem and its solution so that the solution can be expertly carried out by a computer. We don't need computers to engage in computational thinking, but CT can leverage the power of computers to solve a problem.

Computational thinking helps build these skills:

• Decomposition – the process of breaking down a complex problem into smaller parts that are more manageable, and helps us see problems as less overwhelming.
• Abstraction – identifying common features, recognizing patterns, and filtering out what we don’t need. 
• Algorithmic Thinking – designing a set of steps to accomplish a specific task. 
• Debugging and Evaluation – testing and refining a potential solution, and ensuring it’s the best fit for the problem.

These skills relate to critical thinking and problem solving skills across different subject matter, highlighting how concepts of computing can be combined with other fields of study to assist in problem-solving.

Computational thinking is a way of describing the many problem solving skills involved in computer science, including those needed to design, develop, and debug software. However, computer science is more than just skills, it also includes concepts about the Internet, networking, data, cybersecurity, artificial intelligence, and interfaces. Computational thinking can be relevant beyond computer science, overlapping with skills also used in other STEM subjects, as well as the arts, social sciences, and humanities.

Why is computational thinking important? 

Computational thinking can apply these problem-solving techniques to a variety of subjects. For example, CT is established as one of the Science and Engineering Practices in the Next Generation Science Standards , and can also be found in several math state standards . Computational thinking also overlaps with skills used in other STEM subjects, as well as the arts, social sciences, and humanities. Computational thinking encourages us to use the power of computing beyond the screen and keyboard. 

It can also allow us to advance equity in computer science education...

By centering the problem-solving skills that are at the heart of computer science, we can promote its integration with other subject areas, and expose more students to the possibilities of computer science. 

Not only that, but computational thinking also opens the door for us to examine the limitations and opportunities of technology as it’s being developed. We’re able to analyze who is creating technology and why, as well as think critically about the ways in which it can impact society. 

Want to learn more about computational thinking?

To learn more about computational thinking, check out the resources:

• This framework for CS for K-12 places CT at the core of its practices and is what the California standards are based on. 
• Part of the British Computing Society, Computing at School put forth resources to assist teachers in the UK in embedding  CT in their classrooms. 
• This is one of the earliest definitions of CT for educators, and noteworthy for its inclusion of certain dispositions as being essential for effective CT.  
• The developers of Scratch divide CT into concepts, practices, and perspectives, and focus on the expressive and creative nature of computing. 
• Instead of focusing solely on standards for students, ISTE  compiled a set of knowledge, skills, and mindsets needed for educators to be successful in integrating  CT across the K-12 content areas and grade bands.  
• Bebras began as an international competition to promote CT for students, regardless of programming experience. It is now increasingly being used as a form of CT assessment. 

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One of the most important skills you learn in your computer science courses is how to problem solve. Although we cover some general problem solving paradigms in class, the best way to improve these skills is to get practice, practice, and more practice. Different people have different techniques that work best for them; below are some general tips that work for most people.

Please read these suggestions carefully.

Questions the Helpers May Ask You

When you ask a lab helper for their assistance, they will assume you have tried to solve the problem yourself. They will (reasonably) expect that you have tried out the steps outlined in this document; you should therefore be prepared to answer the following questions:

• Did you re-read the prelab and lab?
• Do you understand the problem?
• Have you tried solving some examples by hand?
• (For problems designing a solution) What have you tried? What topic from class does this most ressemble?
• If you can’t solve the problem whole-hog, what small case can you solve?
• (For syntax errors) What line of your code is causing the error? What do you think the compile error means, and what usually causes this kind of problem?
• (For logical errors) On what example does your program consistently break? Have you traced through the program? Which line of your program is not doing what it should?

Four Main Problem Solving Steps:

1. understand the problem..

Solving the right problem is the most important part of problem solving. Be sure, absolutely 100% positively sure, that you understand the problem before attempting a solution. This involves:

• Reading the prelab and lab very carefully (including all bold text, italicized text, and everything else);
• Reviewing class notes on related topics;
• Trying some small examples to make sure you understand what is being asked; if examples are given to you, make sure you understand them before continuing, as they are usually there to help clarify some common misconceptions; and
• Asking someone to help clarify anything that is still confusing.

2. Design a Solution.

Formulate an algorithm to solve your problem. This involves:

• Understanding what is being asked of you. See step 1.
• Draw out some examples. Use paper . How would you solve these small cases, by hand? Is there a method to what you are doing? Try to formalize the steps you are taking, and try to think about whether they would work more generally, in bigger cases. Then try some bigger cases and convince yourself.
• Reread the prelab . Did you already run some examples by hand? Did you have trouble with it then?
• Write down the stuff you know about the problem and the examples you’ve tried, so that you can more easily find patterns .
• Might a recent topic from class help? Usually at least some, if not most, of the lab will make use of recently covered material . Go over that topic, make sure you understand it, then try to make connections to lab.
• Split the problem into smaller (more manageable) chunks, and try to solve the simpler problems. Go as small as you need in order to find some solution. Once you have the smaller problem solved, worry about how to generalize it to a slightly larger problem.
• Just try something , anything, even if it is completely random and obviously wrong. When/if your attempt doesn’t work, it may still give you insight into what may work. It is not as crazy as it initially sounds!
• Use a friend, lab helper, puppet, etc. as a sounding board ; sometimes, just voicing your problem will lead you to the “aha!” moment you need.
• If you are still stuck, step away from the keyboard . Take a walk, go eat dinner or have a coffee. Sleep on it. Not literally. Taking a break is sometimes the most productive thing you can do, trust me.
• Finally, stay positive . Even when things don’t work, you can still gain a better understanding of the problem. Don’t give up, just go with the flow and see where it takes you. Struggling is part of the process!

3. Implement your Solution.

Write the code to solve your problem. This involves

• Understanding the problem, and designing a solution on paper. See steps 1 and 2.
• Translating your design into actual code. Rather than doing this linearly, implement small chunks at a time. Break your code into subroutines, and make sure that each subroutine works before proceeding to the next. Compile and save often .
• If you run into syntax errors, determine which line of your code is causing the problem. You can do this by systematically commenting out blocks of code until you find the block that causes the problem.
• If you run into logical errors (as in, the program compiles but does not do what it is supposed to), find some examples on which your problem consistently fails. Trace through the program line by line, with one of these examples, to figure out exactly which line is not doing what you intend it to.
• If the output doesn’t match what you expect, use print statements to trace through what your program is doing, and compare that to what your program should be doing. Even better, if you know how to use a debugger (in eclipse, for example, use it!)

4. Check your Solution.

This step is often overlooked, but is absolutely crucial. Your program does not necessarily work because it works on the given test cases on the lab. You have to think critically about what you code. This involves

• Certainly check your program on all test cases given to you on the lab and prelab. The prelab often specifically contains hand-solved test cases precisely for this purpose!
• Thinking about the “ boundary cases ,” such as, when would this array go out of bounds? For what indices will this for loop start and end?
• Think: how would this program break ? Then, that failing: how would I convince my skeptical friend it can’t be broken?

Remember: problem solving is a creative process, which cannot be forced. Don’t get angry if you don’t see the answer right away, or you don’t see it as fast as your friend. You will have different strengths, and you can always improve. You will learn from your mistakes, so that’s always a plus!

Last updated July 3rd, 2012 by asharp

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Computer Basics  - Basic Troubleshooting Techniques

Computer basics  -, basic troubleshooting techniques, computer basics basic troubleshooting techniques.

Computer Basics: Basic Troubleshooting Techniques

Lesson 19: basic troubleshooting techniques.

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Troubleshooting

Do you know what to do if your screen goes blank? What if you can't seem to close an application, or can't hear any sound from your speakers? Whenever you have a problem with your computer, don't panic! There are many basic troubleshooting techniques you can use to fix issues like this. In this lesson, we'll show you some simple things to try when troubleshooting, as well as how to solve common problems you may encounter.

General tips to keep in mind

There are many different things that could cause a problem with your computer. No matter what's causing the issue, troubleshooting will always be a process of trial and error —in some cases, you may need to use several different approaches before you can find a solution; other problems may be easy to fix. We recommend starting by using the following tips.

• Write down your steps : Once you start troubleshooting, you may want to write down each step you take. This way, you'll be able to remember exactly what you've done and can avoid repeating the same mistakes. If you end up asking other people for help, it will be much easier if they know exactly what you've tried already.
• Take notes about error messages : If your computer gives you an error message , be sure to write down as much information as possible. You may be able to use this information later to find out if other people are having the same error.

• Restart the computer : When all else fails, restarting the computer is a good thing to try. This can solve a lot of basic issues you may experience with your computer.

Using the process of elimination

If you're having an issue with your computer, you may be able to find out what's wrong using the process of elimination . This means you'll make a list of things that could be causing the problem and then test them out one by one to eliminate them. Once you've identified the source of your computer issue, it will be easier to find a solution.

Let's say you're trying to print out invitations for a birthday party, but the printer won't print. You have some ideas about what could be causing this, so you go through them one by one to see if you can eliminate any possible causes.

First, you check the printer to see that it's turned on and plugged in to the surge protector . It is, so that's not the issue. Next, you check to make sure the printer's ink cartridge still has ink and that there is paper loaded in the paper tray . Things look good in both cases, so you know the issue has nothing to do with ink or paper.

Now you want to make sure the printer and computer are communicating correctly . If you recently downloaded an update to your operating system , it might interfere with the printer. But you know there haven't been any recent updates and the printer was working yesterday, so you'll have to look elsewhere.

You check the printer's USB cord and find that it's not plugged in. You must have unplugged it accidentally when you plugged something else into the computer earlier. Once you plug in the USB cord, the printer starts working again. It looks like this printer issue is solved!

This is just one example of an issue you might encounter while using a computer. In the rest of this lesson, we'll talk about other common computer problems and some ways to solve them.

Simple solutions to common problems

Most of the time, problems can be fixed using simple troubleshooting techniques, like closing and reopening the program. It's important to try these simple solutions before resorting to more extreme measures. If the problem still isn't fixed, you can try other troubleshooting techniques.

Problem: Power button will not start computer

• Solution 1 : If your computer does not start , begin by checking the power cord to confirm that it is plugged securely into the back of the computer case and the power outlet.
• Solution 2 : If it is plugged into an outlet, make sure it is a working outlet . To check your outlet, you can plug in another electrical device , such as a lamp .

• Solution 4 : If you are using a laptop , the battery may not be charged. Plug the AC adapter into the wall, then try to turn on the laptop. If it still doesn't start up, you may need to wait a few minutes and try again.

Problem: An application is running slowly

• Solution 1 : Close and reopen the application.

Problem: An application is frozen

Sometimes an application may become stuck, or frozen . When this happens, you won't be able to close the window or click any buttons within the application.

• Solution 2 : Restart the computer. If you are unable to force quit an application, restarting your computer will close all open apps.

Problem: All programs on the computer run slowly

• Solution 2 : Your computer may be running out of hard drive space. Try deleting any files or programs you don't need.
• Solution 3 : If you're using a PC , you can run Disk Defragmenter . To learn more about Disk Defragmenter , check out our lesson on Protecting Your Computer .

Problem: The computer is frozen

Sometimes your computer may become completely unresponsive, or frozen . When this happens, you won't be able to click anywhere on the screen, open or close applications, or access shut-down options.

• Solution 3 : Press and hold the Power button. The Power button is usually located on the front or side of the computer, typically indicated by the power symbol . Press and hold the Power button for 5 to 10 seconds to force the computer to shut down.
• Solution 4 : If the computer still won't shut down, you can unplug the power cable from the electrical outlet. If you're using a laptop, you may be able to remove the battery to force the computer to turn off. Note : This solution should be your last resort after trying the other suggestions above.

Problem: The mouse or keyboard has stopped working

• Solution 2 : If you're using a wireless mouse or keyboard, make sure it's turned on and that its batteries are charged.

Problem: The sound isn't working

• Solution 1 : Check the volume level. Click the audio button in the top-right or bottom-right corner of the screen to make sure the sound is turned on and that the volume is up.
• Solution 2 : Check the audio player controls. Many audio and video players will have their own separate audio controls. Make sure the sound is turned on and that the volume is turned up in the player.
• Solution 3 : Check the cables. Make sure external speakers are plugged in, turned on, and connected to the correct audio port or a USB port. If your computer has color-coded ports, the audio output port will usually be green .

Problem: The screen is blank

• Solution 1 : The computer may be in Sleep mode. Click the mouse or press any key on the keyboard to wake it.
• Solution 2 : Make sure the monitor is plugged in and turned on .
• Solution 3 : Make sure the computer is plugged in and turned on .
• Solution 4 : If you're using a desktop, make sure the monitor cable is properly connected to the computer tower and the monitor.

Solving more difficult problems

If you still haven't found a solution to your problem, you may need to ask someone else for help. As an easy starting point, we'd recommend searching the Web . It's possible that other users have had similar problems, and solutions to these problems are often posted online. Also, if you have a friend or family member who knows a lot about computers, they may be able to help you.

Keep in mind that most computer problems have simple solutions, although it may take some time to find them. For difficult problems, a more drastic solution may be required, like reformatting your hard drive or reinstalling your operating system. If you think you might need a solution like this, we recommend consulting a professional first. If you're not a computer expert, it's possible that attempting these solutions could make the situation worse.

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18 Skills All Programmers Need to Have

Are you an aspiring programmer, or perhaps just interested in learning more about the programming field? Read on to learn more about the hard and soft skills that programmers need to succeed.

Technology has become the backbone of our everyday lives, and programmers are needed to keep moving that technology forward. The options are endless: an aspiring programmer can bring the next life-changing smartphone app to life, create new worlds in gaming, or craft the way millions of people across the globe interact and communicate online. These are just a few of the ways programmers impact the world around them, but all programmers have certain things in common — the in-demand hard and soft skills that propel their career success.

Hard Skills

• Data structures and algorithms
• Database and SQL
• Object-oriented programming (OOP) languages
• Integrated development environments (IDEs)
• Cloud computing
• Web development
• Text editors
• Git version control

Soft Skills

• Communication (verbal and written)
• Teamwork and conflict resolution
• Problem solving
• Adaptability
• Accountability
• Time management

9 Hard Skills Programmers Need

1. Data Structures and Algorithms

Many programmers think that data structures and algorithms (DSAs) are just something you have to “get through” in school, but will never need in real life. However, they’re surprised when so many interviews include DSA questions. There are several reasons companies are interested in a prospective employee’s DSA knowledge, and why programmers should be interested in it too.

For many companies, such as Meta, Google, Microsoft, and Amazon, writing code is just the final step in a long process. The majority of a programmer’s time is actually spent considering the best way to approach a project, including the best data structures and optimal algorithms to employ. These decisions have a real impact on the company’s resource usage and profitability, so it’s no surprise that DSAs figure prominently in their interview process. And, even for companies outside of Silicon Valley, these questions are important because they demonstrate a programmer’s foundational knowledge and problem solving abilities.

Once a programmer has the position, DSAs still play a role in day-to-day work. Specifically, data structures are a particular way of organizing data so that it can be used most effectively, and there are many to choose from . One of the most commonly used data structures is an array , which holds and indexes items of the same data type such as integers. Additional types of data structures include linked lists , which organize data into linear, sequentially-linked order; and stacks , which allow programmers to access recently placed items first, as if they were picking up the first book in a pile. 

Algorithms , meanwhile, are a set of instructions programmers give to computers to solve a problem, much like the recipe one might give a cook. These step-by-step guidelines can perform a variety of tasks, including searching and sorting data in a way that is ordered and makes sense.

In addition, many startups, as well as FAANG employers , look for programmers who possess the agility to scale programs and innovate through the use of DSAs.

2. Database and SQL

One of the basic expectations of any programmer is that they are familiar with core database concepts. This is because data is the fuel companies run on, and it proliferates almost every aspect of every project. 

While there are many languages used to work with databases, the most common is Structured Query Language (SQL — pronounced “sequel”). Though SQL was developed in the 1980s, it is still the standard language used to communicate with relational databases and is considered critical for modern programmers. In recent years, SQL has been heavily used by PC databases because it facilitates access to distributed databases (e.g., those spread out over multiple computer systems); allowing several local users to access the same network simultaneously. SQL also enables easy storage and organization of data in relational databases (e.g., databases where tables are related to one another through common data). 

If you’re interested in gaining SQL experience, it may be helpful to practice with MySQL . Referred to as a relational database management system (RDMS), this open-source software is based on SQL and many aspiring coders use it to work on developing their own systems, applications, and websites for free.

NoSQL , on the other hand, is a database management system (DBMS) that stores and accesses data using key-values, rather than relationally, which offers some additional flexibility. One example of a NoSQL database is MongoDB , an open-source program which can be used for high-volume document data storage, and deals with document structure variations nicely.

Of course, there are many more systems and software packages to learn when mastering databases, but having a strong foundation in database concepts and SQL is an important first step for all programmers.

3. Object-oriented programming (OOP) languages

OOP languages support a way of programming (sometimes called a paradigm) that relies on classes and objects. Think of classes like groups of similar things, such as fruits, with objects that tell us more about individual items in that class, such as apples. This programming paradigm is important because it allows programmers to easily reuse complex code across programs. For example, if I say “my apple,” it isn’t necessary for me to tell you all the attributes of my apple (i.e., red, round, grew on a tree, belongs to me). Similarly, by using an object (myapple) from a class (fruit), a programmer can easily communicate instructions or information across multiple programs, enabling more effective and efficient coding as a result.

For this reason, OOP languages such as Java , C++ , Python , and Perl are important for programmers, and they need to have at least one in their skill set.

In addition, such languages as JavaScript and PHP pair well with OOP languages to further enhance efficiencies and functionality.

4. Integrated Development Environments (IDEs)

Combining a variety of developer tools through a single graphic user interface (GUI), IDEs are a workbench for programmers where all the tools they need are laid out and ready for them to use — kind of like a workbench with a saw, drill, nails, and a hammer if you were planning to build a birdhouse.

IDEs are valuable in that by learning one IDE, a developer can become familiar with a variety of tools that work synergistically, rather than learning each tool separately and pulling together the right tools for each coding task. In addition, because all the tools are available through one GUI, the programmer doesn’t have to spend time switching between applications.

It’s important to note that IDEs are language-specific, meaning that an IDE may be designed to work with one or more programming languages. Here is a quick rundown of some of the more popular IDEs and the languages they work with (listed alphabetically).

• AWS Cloud9 : Supports over 40 languages , including JavaScript, Python, PHP, Ruby, Go, and C++ 
• Code:: Blocks : Supports C and C++
• Eclipse : Supports Java
• Eclipse Theia : Supports over 60 languages, including JavaScript, Java, and Python
• GNAT Studio : Supports Ada, SPARK, C, C++, and Python
• IntelliJ IDEA : Supports Java, but understands many other programming languages, including Groovy, Kotlin, Scala, JavaScript, TypeScript, and SQL
• NetBeans : Supports several languages including, Java, PHP, JavaFX, and JavaScript
• PyCharm : Supports major Python frameworks such as Flask, Django, web2py, Pyramid, and Google App Engine 
• SlickEdit : Supports over 70 languages , including C++, Java, HTML, PHP, JavaScript, Python, Perl, and Ruby
• Xcode : supports Swift, but allows coding in C, C++, Objective-C, Objective-C++, Java, Applescript, Python, React.js, and Ruby
• Visual Studio : Supports C, C++, C++/CLI, Visual Basic .NET, C#, F#, JavaScript, TypeScript, XML, XSLT, HTML, and CSS
• Visual Studio Code : Supports many languages including, C++, C#, CSS, Dart, Dockerfile, F#, Go, HTML, Java, JavaScript, JSON, Julia, PHP, Python, SCSS, T-SQL, and TypeScript.

It’s also important to remember that while cloud-based IDEs aren’t constrained by the programmer’s operating system, this is a use constraint for IDEs that aren’t cloud-native.

5. Cloud computing

Cloud computing is experiencing explosive growth, as cloud developers are needed for all businesses who wish to migrate their environments, storage, and digital assets to the cloud. In fact, according to LogicMonitor , 87% of global IT decision makers agree that the COVID-19 pandemic has accelerated cloud migration for most organizations. In addition, once migrated, businesses will need programmers familiar with the technology necessary to work effectively with cloud-native applications. And, as businesses rely more heavily on data science, machine learning, and artificial intelligence, work in the cloud becomes even more important since algorithms and models consume significant resources. The result of these business transitions and needs is that cloud engineers and developers, as well as cloud-savvy programmers, are in high demand.

The good news is that many of the languages needed for cloud computing are already top languages for programmers, including:

In addition, it’s a good idea for programmers to familiarize themselves with cloud platforms, such as: 

• Amazon Web Services (AWS)
• Microsoft Azure
• Google Cloud Platform (GCP)

Even focusing on just one, to learn key functionality, will help you gain a better understanding of how the others work, adding value to your skill set.

6. Web development

Many professionals consider web development a subset, or specialization of programming. Therefore, it only makes sense for those who plan on working in web development to learn the associated languages and tech, right? Well, maybe not.

Certainly, it goes without saying that programmers who plan to work in web development need to have a strong background in the core tools. Some of these tools include:

• HTML/CSS : HyperText Markup Language (HTML) and Cascading Style Sheets (CSS) are both basic coding languages — often, they are the first two that web developers learn. HTML helps organize the content and structure of a web page, while CSS determines its style and presentation.
• JavaScript : JavaScript is most commonly used for front end development, though it is sometimes used for back end development as well. As one writer for Mozilla explains, “Every time a web page does more than just sit there and display static information for you to look at — displaying timely content updates, interactive maps, animated 2D/3D graphics, scrolling video jukeboxes, etc. — you can bet that JavaScript is probably involved.”
• API : An API (Application Programming Interface) is the part of a remote server that receives a user’s requests and sends responses to the rest of the server and website. Programmers set up a website’s API to complete user requests and connect them to an external server without leaving the original site. Having familiarity with APIs ranks high on any web development skills list because it helps improve a customer’s experience on websites.
• PHP : PHP (Hypertext Preprocessor) is a highly accessible, general-purpose scripting language that can be easily embedded into HTML to accentuate front end programming efforts. Unlike JavaScript, PHP is executed entirely on the server-side , rather than the client-side .

With that said, even programmers who don’t plan on working in web development can benefit from understanding the basics. For example, many projects that programmers work on include a web component. With some foundational knowledge of web development concepts, concerns, and constraints, programmers are better able to understand how data will be collected and used, what functionality may be required at a later date, and how enterprise systems may be impacted in the future. Certainly, this knowledge will help programmers have a more comprehensive understanding of not only the best way to develop their own portion of the project, but also how to offer additional solutions to those whose expertise is focused on client-side functionality.

7. Containers

Containers are preconfigured environments that package code and other dependencies an application needs to run, without the need for downloads to a physical computer. Unlike traditional methods where code is developed in a specific computing environment and transferred to a new location resulting in bugs (i.e., virtual machines ), containers bundle the application code with related configuration files, libraries, and dependencies which minimizes the potential for bugs. In addition, because the operating system (OS) files are included, containers virtualize the operating system and the application can run anywhere. As a result, programmers are able to develop and deploy applications in a faster and more secure manner. 

A basic example of container usage in education is a teacher preparing for their Python programming class . By using a container, they are able to grab the necessary application, libraries, and dependencies (including the OS), making it easier to prepare while ensuring learners will have the necessary access to learn Python from anywhere. 

Some of the most popular container management software includes:

• AWS Fargate
• Google Kubernetes Engine
• Linux Containers
• Microsoft Azure Container Services

8. Text editors

Text editors are programs that enable the opening, viewing, and editing of plain text files. Because text editors do not add formatting to text, like word processing programs do, programmers can use text editors to easily write and edit in programming and markup languages. In addition, text editors help programmers create documentation files and maintain configuration files.

Some of the most frequently used text editors include:

• Visual Studio Code
• Sublime Text

Git is a version control system that allows programmers to manage and track changes to source code throughout the development process. It makes it easy to correct any errors that may occur because every version is saved and can be recalled on demand. And, using version control encourages programmers to innovate through trial and error, as they don’t have to worry about losing previous coding attempts.

Git is the most widely used version control system among employers, so it’s important to be well versed and ready to use it when approaching a career in programming.

9 Soft Skills Programmers Need

Soft skills are different from technical (hard) skills in that they are a combination of personal attributes and interpersonal skills that enable professionals to work more effectively and more harmoniously with others.

Here are a few of the most valuable programmer soft skills:

1.  Communication : The ability to explain ideas or work methods clearly, ask and answer questions productively in a group setting, and help reduce conflict through respectful dialog is important to succeeding in coding.

2.  Teamwork and conflict resolution : Constructively sharing ideas, and supporting others’ ideas in turn, is a key element in team success. But would it surprise you to know that consistent agreement isn’t always beneficial? In fact, it’s actually the differing backgrounds and ideas each team member brings to the table that helps a team yield a better result than individual outcomes. Specifically, it’s how differing ideas are discussed, tested, and applied (as a group) to reach a common goal that makes for great collaboration and outstanding results.

3.  Problem Solving : Problem-solving skills are just as important for programmers as technical ability. As Dominique Simoneau-Ritchie, the Director of Engineering at Lever, wrote for HackerNoon , “The more senior you are, the more you’ll be expected to take on complex, poorly defined problems, often with very little context. The true secret to increasing your impact is learning how to tackle a problem of any size and breaking it into manageable pieces that you can successfully solve.”

4.  Empathy : The ability to truly understand the thoughts, feelings, and experiences of another, without judgment, is a vital skill for programmers. Empathy for program end users will result in software with higher satisfaction levels and better user acceptance. And, empathy for team members will not only enhance team connections, but will also foster a culture of trust and mutual assistance. It’s no wonder that so many companies rank empathy as a top 5 soft skill.

5.  Patience : It’s a virtue — but not for the reason you might think. Patient people tend to be less stressed when dealing with obstacles. Studies have shown that cortisol (a stress hormone) negatively impacts cognitive performance, perception, and organizational skills , which are critical to successful coding. As a result, patience (or a lack thereof) can significantly impact project outcomes and coding quality.

6.  Curiosity : “The best developers tend to be naturally curious people who love to learn,” CodeFights CEO Tigran Sloyan writes for Tech Beacon . This skill is likely what drives their ongoing exploration, iterative testing of various ideas, and actively seeking new ways to improve, which are key drivers in a programmer’s growth and success.

7.  Adaptability : If there is one thing that’s constant in programming, it’s that everything changes. Technology evolves, new versions of software release, requirements change, and clients’ needs multiply. For this reason, it is imperative that programmers be adaptable and resilient when it comes to dealing with change and occasional setbacks. Having the ability to calmly assess what needs to be done and adapt is key to success in this field.

8.  Accountability : Many wrongly associate accountability with “blame,” but when used effectively, it is actually something quite different. Accountability begins before a task is assigned or a single line of code is written — it is simply the building of trust between teammates through public discussion of direction, design, and timelines. Specifically, that trust translates into each teammate committing to doing their best work, quickly letting the team know if there is an unanticipated obstacle, and knowing that teammates will work together to address the obstacle in the best way possible. By working transparently and setting collective goals and timelines, accountability is a support — not a sword. Professionals can demonstrate this skill by truly supporting their teammates in a mutual fashion to achieve their overall goals. In fact, the popularity of agile methodology through Scrum project management is an excellent example of the correct application of accountability. 

9.  Time management : Whether it’s a client deadline, a team deliverable, or available budget hours, programmers must be able to manage their time effectively. This includes everything from estimating time to complete a task, helping the team agree on deliverable timelines, or completing individual tasks on time. It also includes knowing when you are running behind and asking a team member for help. Making time management a priority not only makes you more productive as an individual, but it also makes you a better, more reliable team member. Consequently, this is why employers consider this soft skill so important.

Becoming a Programmer

Career prospects for programmers look bright. According to CareerOneStop , an expected 9,700 U.S. job openings in programming are anticipated each year through 2030 with a median salary of $89,190. 

Even better, in the Denver, Colorado, area, programmers can anticipate a median salary of $91,550 and companies such as Meta (formerly Facebook), Intel, Honeywell, Lockheed Martin, and Colorado State University are all actively looking for programmers ,  

To take advantage of these great opportunities, you’ll need to acquire the knowledge and skills programmers need to be successful. The good news is that there are several options to choose from — a traditional degree, independent study, or a coding boot camp.

Obtaining a degree in computer science is always a popular choice for those interested in pursuing a career in programming. Taking three to four years, these degrees allow learners to explore the theoretical aspects of programming, while pursuing adjacent subject matter and additional interests. Given the significant time and financial commitment required to pursue this type of degree, it’s important to be sure it’s the right path for you. Some learners consider taking an introductory coding course, boot camp, or conducting independent study prior to committing to a degree program.

For those who lack the time or financial resources required to pursue a traditional degree, or want to explore their options before making a commitment, independent study can be the right choice. Also, many who prefer a slower tempo and self-directed approach pursue independent study of a programming language to enhance their existing skill set. Common options include:

• MIT OpenCourseWare  
• The Odin Project

Interestingly, many who begin their coding journey in independent study soon progress to enrolling in a coding boot camp. Boot camps are a great place to gain in-demand skills in a practical environment where learners apply their newfound knowledge on real-world projects that will eventually populate their professional portfolios. 

Regardless of the educational path you choose, the right combination of in-demand hard and soft skills will fuel your progress toward a rewarding career in programming.

Are you ready to take the next step and gain the in-demand technical skills needed for a successful web development career? Consider University of Denver Coding Boot Camp — learn critical programming languages, put them into practice on real-world projects to populate your professional portfolio, and hone your soft skills working collaboratively with your classmates. Start your future in programming today!

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How to List Problem-Solving Skills on a Resume [List Included]

Problem-solving skills are more in-demand than ever. 

Employers love candidates with problem-solving skills because, in 99% of cases, they guarantee you're also logical, creative, clear-headed, and a great decision-maker. 

But claiming you have organizational skills on your resume is not enough. 

To impress recruiters, you've got to prove that you possess them. 

This includes understanding which problem-solving skills you possess and adding them to your resume (the right way), among other things.

This is where this article comes in! We put together everything you need to know about problem-solving skills, including: 

• 8 Essential Problem-Solving Skills for Your Resume

How to Add Problem-Solving Skills to Your Resume

• Why Are Problem-Solving Skills Important
• 6 Problem-Solving Steps

Let's dive right in! 

8 Problem-Solving Skills for Your Resume

Research shows that problem-solving skills consist of several facets : 

• Identifying and analyzing a problem
• Taking effective actions
• Understanding the effect of the decisions
• Coming up with creative and novel solutions
• Transferring knowledge from one situation to another
• Thinking abstractly about problems

As such, there is no single problem-solving skill. Problem-solving includes a set of skills, all of which are equally important in helping your personal and professional life. 

Below, we’ll cover the eight most important problem-solving skills that you can also list on your resume to impress recruiters: 

#1. Research skills

To properly identify and understand a problem, you need excellent research skills. 

Research skills involve being able to gather information from the right sources, reviewing that information in detail to extract the data you need, analyzing the data according to the context, and being able to apply the data to your situation. 

#2. Analytical skills

Analytical skills are required throughout the entire process of solving a problem. 

In a nutshell, analytical skills refer to being able to analyze a situation in depth and from different perspectives . Specifically, you need analytical skills to achieve all of the following while solving a problem:

• Detect patterns
• Interpret data
• Analyze new information
• Reach conclusions based on several factors

#3. Creativity

Being creative means being able to think outside of the box and look at situations and problems inventively. 

For most people, creativity is mainly associated with creative industries such as arts and crafts, architecture, design, etc. 

In reality, however, creativity is an essential success factor for every job and the data is here to support that. According to this Adobe study , problem-solving (51%) and creativity (47%) have gained the most value in driving salary increases in the last five years. 

When it comes to the process of solving a problem, creativity can help you consider more perspectives, think abstractly about problems, and come up with novel solutions that others haven’t thought of before.

#4. Critical thinking skills

Being able to think critically means that you’re good at rationalizing, understanding the connections between ideas or situations, and logically analyzing any given situation. 

As such, strong critical thinking skills can help you see beyond what’s at face value, make more informed decisions, and anticipate the outcomes of said decisions. 

People who have critical thinking skills share traits such as open-mindedness , cognitive flexibility , skepticism , clarity , and precision . 

#5. Decision-making skills

Before coming up with a single action plan to solve a problem, you’ll need to first brainstorm several possible solutions. 

After that, you need good decision-making skills to choose the best possible solution. Without decision-making skills, you risk prolonging finding a proper solution or aggravating a problem even more. 

#6. Communication skills

With strong communication skills , you’re able to successfully explain the problem to others and propose your solutions. In turn, you can be sure that everyone’s on the same page and that you’re carrying out the action plan accordingly. 

Some communication skills required for problem-solving include: 

• Active listening
• Written and verbal communication
• Giving and receiving feedback

#7. Collaboration

Problem-solving is rarely a process you carry out alone. More often than not, you need to consult relevant stakeholders, give and receive feedback, and work with a team towards a common goal (i.e. solving the problem).

Well, collaboration entails exactly that - working well with others, cooperatively addressing problems, and putting a group’s goal ahead of personal goals. 

Some important collaboration skills that help with problem-solving include: 

• Conflict resolution
• Emotional intelligence 

#8. Attention to Detail 

Have you ever heard of the expression “the devil’s in the details?”

It means that something may seem simple on the surface, but in fact, the details make it complicated and are likely to cause problems.

Well, if you’re someone who shows great attention to detail, you’re not likely to let details keep you from solving a problem effectively. 

Not to mention, being able to spot and understand even the smallest details that make up a problem means you’ll be able to grasp the issue in its entire complexity and come up with even more inventive and workable solutions. 

Now that we covered the most important problem-solving skills, we’ll show you how to add them to your resume so that you can stand out from other candidates. 

Let us walk you through the process, step-by-step: 

#1. Mention Your Problem-Solving Skills on Your Resume Summary

The resume summary is a three or four-sentence paragraph positioned at the top of your resume that includes: 

• Your profession and years of experience 
• Your top skills (i.e. hard skills or soft skills)
• One or two noteworthy achievements 

The goal of the resume summary is to catch the hiring manager’s attention, show them you’re a relevant candidate and get them to go through the rest of your resume in detail. 

As such, it’s your first chance to highlight your problem-solving skills effectively. You can either do that by mentioning them among your top skills or by mentioning an achievement that proves you possess a given skill.

In the best-case scenario, you can even do both. 

Here is an example of how you can include problem-solving skills in your resume summary: 

• Behavioral psychologist with 7+ years of experience in the field. Great research, analytical, and communication skills. Over the last eight years, I’ve worked closely with more than 100 patients with different behavioral disorders, helping them improve their personal and professional lives through different treatment methods. 

#2. Add the RIGHT Problem-Solving Skills Under Your Soft Skills

Secondly, you should list your problem-solving skills under your resume’s soft skills section . 

The listing part is pretty easy - simply create a section titled Skills and write down your problem-solving skills.

There is, however, one caveat: 

You don’t want to overkill your skills section by listing every problem-solving skill we covered in this article.

Not only will the hiring manager have trouble believing you possess each and every skill, but there’s also a high chance you don’t even need all those skills to begin with. 

To make your skills section as relevant as possile, do the following: 

• Check the job description. The job description can show you exactly what skills you need for the job. If you’re applying for, say, a software engineering position, you’ll probably be required to have the following problem-solving skills: analytical skills, creativity, attention to detail, and cognitive flexibility. 
• Identify the skills you possess. Think about which skills you can back up with actual experience from your previous jobs. Only list problem-solving skills that you actually possess and that you can prove you possess on your resume. 
• Add those skills under your soft skills. Then, add the problem-solving skills that you have and that are required in the job under your resume’s “Soft Skills” section. 

#3. Prove Your Problem-Solving Skills In Your Work Experience Section

Finally, you should use the work experience section to prove that you’ve got the problem-solving skills you’ve mentioned throughout your resume. 

Anyone can just claim that they’ve got problem-solving skills on their resume - not everyone can back them up with experience.

Here’s what you can do to convey that you possess problem-solving skills and also make your work experience section as impactful as possible: 

• Tailor your work experience to the job. Only add past jobs that are relevant to the position you are applying for now. If you’re applying for, say, a software engineering position, the hiring manager will be interested in your previous jobs in the field, but probably not too interested in the time you worked as a server at a restaurant. 
• Focus on your achievements instead of your responsibilities. More often than not, hiring managers know exactly what your responsibilities consisted of in previous jobs. What they want to know is how you made a positive impact with your achievements. 
• Make your achievements quantifiable. Speaking of achievements, you want to make them as quantifiable as possible. After all “treated ten patients in the course of a year using positive reinforcement” sounds much better than “treated ten patients.”
• Use the Laszlo Bock formula . If you’re having trouble phrasing your achievements, the following formula will probably be of help: “Accomplished X as measured by Y doing X.” 
• Leverage action verbs and keywords. There are hundreds of words and verbs you can use instead of “did,” “accomplished,” etc. The more descriptive you are of your achievements, the more impressive they can sound.

And here’s an example of a project manager describing their problem-solving skills in their work experience section:

• Fixed company communication issues by implementing a new project management solution. 
• Improved team productivity by implementing time-tracking software and doing daily stand-up calls.
• Managed to meet all client deliverable deadlines in 2022.

Why Are Problem-Solving Skills Important?

Are you wondering what exactly is it that makes problem-solving skills so important? 

After all, there are hundreds of soft skills out there that you can master, improve, or learn how to add to your resume. So it’s normal to wonder “why should I focus on problem-solving?” 

Here is why problem-solving skills matter:

• They can improve your employability. Problem-solving skills are among the most important skills to employers across a range of occupations. In short, employers are always looking for proactive thinkers who can address professional challenges.
• They can help you grow in your career more easily. You’ll be more likely to get promoted if you can come up with creative solutions to the different problems that you’ll face throughout your career.
• They can become an essential part of your personal brand . Your current employer, coworkers, and future employers alike will see you as someone creative, reliable, and helpful.
• They are related to a range of other valuable skills. When you prove you’re a problem solver, you’re effectively saying you’re attentive to detail, logical, creative, analytical, curious, and other things employers are looking for in their employees.

10 Jobs That Require Problem-Solving Skills

As we’ve already mentioned, problem-solving skills come in handy for practically every job. 

Whether you’re a teacher who needs to solve a dispute between peers in your class or a customer representative who needs to help a client, knowing how to go about solving issues is definitely an asset. 

That said, some jobs are all about solving problems. In such cases, problem-solving skills are not just a nice addition to have on your resume - they’re crucial to getting hired. 

Here are the top 10 jobs requiring problem-solving skills in 2024: 

• Software engineer
• Air-traffic controller
• Police officer
• Social worker
• Psychologist
• UX designer

35 Action Verbs You Can Use to Highlight Your Problem-Solving Skills

The language you use to describe your problem-solving skills matters.  

Sure, you can use “ solved” to describe how you dealt with a problem throughout your entire resume and risk coming off as repetitive and unimaginative. 

Or , you can use any of the following action verbs and keywords and make your problem-solving skills pop out in the eyes of recruiters: 

• Calculate  
• Critically think 
• Draw conclusions
• Experiment 
• Listen/Listen actively 

The Problem-Solving Process in 6 Steps

Problem-solving is a methodical process. It consists of certain steps that you always need to take if you want to find a good solution. 

The more you understand and practice this process, the better you can get at solving problems. 

Below, we cover the six main steps of problem-solving in detail:

#1. Identify the problem 

The first step to solving a problem is identifying exactly what’s causing it. 

After all, if you’re not focusing on the real underlying issue, you might come up with solutions that don’t fit the problem itself. 

Say, for example, that you’re a teacher that’s facing poor class performance. Identifying whether the problem comes from the students’ not studying enough or from your own teaching methods can make a big difference in the solutions you come up with. 

It typically happens that the faster you find the root cause of the problem, the easier it is to find a proper solution. 

#2. Understand the problem

Once you identify the problem, you’ve got to understand it completely. Here are some questions you can ask to make sure you properly understand a problem: 

• What is the scale of the problem? 
• What are its short and long-term effects? 
• Have you faced something like this before?
• Can the problem be solved by dividing it into smaller parts?

The better you understand the problem in its complexity, the more likely you are to come up with effective solutions. 

#3. Research the systems that make up the problem 

In many cases, solving a problem will be a complex undertaking. See, complex problems are often the result of several different underlying systems that you need to understand to find a dynamic solution. 

Let’s take the teacher example from above. 

If a certain student is not doing too well and keeps getting poor grades, you might be tempted to go the easy route and simply chastise them and tell them to study more.

This, in a lot of cases, might simply not work because you’re not addressing the root cause of the problem.

The student might, for example, be burned out , unmotivated by the curriculum, or simply struggling with specific topics.

A problem-solving solution that’s more likely to work would be to talk to the student (or their parents), try to understand the reason for their poor grades, and address the root cause behind the problem itself.

#4. Visualize the problem 

This may not apply to all situations, but it can definitely come in handy for most. 

Drawing a diagram to visualize the situation or your solution to the problem can help you grasp its complexity better - especially if the problem is multi-faceted. Anything from PowerPoint to a piece of white paper can be a good tool to visualize your problem, highlight the problem area, and tackle it more effectively.

#5. Brainstorm solutions 

After you’ve done all the above, it’s time to start thinking about solutions. 

This is another step of the problem-solving process that’s based on collaboration and effective communication. In the brainstorming phase, you should sit with team members or relevant stakeholders and come up with as many creative ideas and solutions as possible. 

This is not where you come up with your most refined, well-thought-out ideas. Instead, it’s where you discuss freely and combine diverse knowledge and analysis of the problem to come up with diverse solutions. 

Brainstorming is an essential part of problem-solving that can help you break out of boring or predictable ideas and thinking patterns. 

#6. Choose the best answer(s)

This is where decision-making skills come in. With a list of different potential solutions, you can narrow down your options to finally choose the best one. 

To reach a solution more easily, take the following into consideration:

• Your company’s/organization’s objectives
• The budget and the timeframe at your disposal
• The success outcomes
• Potential risks linked to the solution 

Finally, discuss your solutions with relevant stakeholders and team members to gather all the possible feedback that can help you make the best possible decision. 

And remember - once you’ve chosen the best possible solution to a problem, your work is far from over. Being a problem solver also includes the following: 

• Develop and implement an action plan
• Monitor the progress of your plan 
• Make necessary adjustments during the process
• Evaluate the outcomes of your solution 

Problem-Solving Skills Resume Example

Want a resume that makes your problem-solving skills pop like the above example? 

Use one of our tried-and-tested resume templates . 

They’re free, modern, and created in collaboration with some of the best HR professionals from around the globe!

Key Takeaways 

And that's a wrap on problem-solving skills. By now, you should know everything there is to know on the topic. 

Before you go, here are the main points we covered in this article: 

• Problem-solving skills are a set of soft skills that help you solve problems effectively. They involve critical thinking, analytical skills, creativity, communication skills, and attention to detail. 
• Problem-solving skills can improve your employability, work performance, and personal brand. 
• Add your problem-solving skills to your resume summary, under the soft skills section, and in your work history section. 
• When you’re creating your work history section, make sure to tailor it to the job, focus on your achievements and make them quantifiable, and use action verbs and keywords from the job description. 
• To get better at solving problems, follow these steps: identify and understand the problem, research the systems that make up the problem, visualize the problem, brainstorm, and choose the best possible solution. 
• Once that’s done, create an action plan and make sure to monitor its progress as you’re implementing it. 

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Deaf in STEM: A New Approach to Measuring Problem Solving, Deductive Reasoning, Creativity, and Ability

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• Shireen Hafeez 26  

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14698))

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Deaf Kids Code (DKC), founded in 2015 by Shireen Hafeez, is a national organization aimed at equipping Deaf, deaf, and hard of hearing students with computer science, technology, and design thinking skills to enhance their societal and economic participation. Utilizing Deaf role models from diverse professional backgrounds, DKC has reached over 15,000 participants across 50 locations nationally, extending its impact internationally as well. The organization adopts a kinesthetic learning model, emphasizing experiential learning and fostering a growth mindset among students, positioning them as creators, not just consumers, of technology. DKC’s objectives include expanding STEM/CS learning opportunities, inspiring students towards STEM careers, and advocating for integrating such education into deaf education curricula. Collaborations with industry giants like Scratch Foundation, Microsoft, Google, AWS, and Dell Tech Crew bolster DKC’s efforts, while interventions like ASL-accessible digital tools and partnerships with educational institutions and corporations further bridge the digital divide for the Deaf community. Through internships, shadowing opportunities, and specialized workshops, DKC facilitates hands-on learning experiences and career pathways in technology for Deaf individuals, paving the way for greater inclusivity and empowerment in the tech industry.

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Hafeez, S. (2024). Deaf in STEM: A New Approach to Measuring Problem Solving, Deductive Reasoning, Creativity, and Ability. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. HCII 2024. Lecture Notes in Computer Science, vol 14698. Springer, Cham. https://doi.org/10.1007/978-3-031-60884-1_4

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