big data analytics assignment questions

Big Data Analytics

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What is Big Data Analytics?

Uncover the realm of big data analytics: its impact, tools, challenges, and real-world applications across industries. Dive into data-driven insights now.

What is big data analytics?

How does big data analytics work, the importance of big data analytics, types of big data analytics, the benefits of big data analytics, the challenges of big data analytics, big data analytics examples, common big data analytics techniques, harness your big data with amplitude analytics.

Big data analytics examines and analyzes large and complex data sets known as “big data.”

Through this analysis, you can uncover valuable insights, patterns, and trends to make more informed decisions. It uses several techniques, tools, and technologies to process, manage, and examine meaningful information from massive datasets.

We typically apply big data analytics when data is too large or complicated for traditional data processing methods to handle efficiently. The more information there is, the greater the need for diverse analytical approaches, quicker handling times, and a more extensive data capacity.

Predictive Analytics - Prescriptive Analytics - Descriptive Analytics - Diagnostic Analytics

Big data analytics combines several stages and processes to extract insights.

Here’s a quick overview of what this could look like:

• Data collection : Gather data from various sources, such as surveys, social media, websites, databases, and transaction records. This data can be structured, unstructured, or semi-structured.
• Data storage : Store data in distributed systems or cloud-based solutions. These types of storage can handle a large volume of data and provide fault tolerance.
• Data preprocessing : It’s best to clean and preprocess the raw data before performing analysis. This process could involve handling missing values, standardizing formats, addressing outliers, and structuring the data into a more suitable format.
• Data integration : Data usually comes from various sources in different formats. Data integration combines the data into a unified format.
• Data processing : Most organizations benefit from using distributed frameworks to process big data. These break down the tasks into smaller chunks and distribute them across multiple machines for parallel processing.
• Data analysis techniques : Depending on the goal of the analysis, you’ll likely apply several data analysis techniques. These could include descriptive , predictive , and prescriptive analytics using machine learning, text mining, exploratory analysis, and other methods.
• Data visualization : After analysis, communicate the results visually, like charts, graphs, dashboards, or other visual tools. Visualization helps you communicate complex insights in an understandable and accessible way.
• I nterpretation and decision making : Interpret the insights gained from your analysis to draw conclusions and make data-backed decisions. These decisions impact business strategies, processes, and operations.
• Feedback and scale : One of the main advantages of big data analytics frameworks is their ability to scale horizontally. This scalability enables you to handle increasing data volumes and maintain performance, so you have a sustainable method for analyzing large datasets.

It’s important to remember that big data analytics isn’t a linear process, but a cycle.

You’ll continually gather new data, analyze it, and refine business strategies based on the results. The whole process is iterative, which means adapting to changes and making adjustments is key.

Big data analytics has the potential to transform the way you operate, make decisions, and innovate. It’s an ideal solution if you’re dealing with massive datasets and are having difficulty choosing a suitable analytical approach.

By tapping into the finer details of your information, using techniques and specific tools, you can use your data as a strategic asset.

Big data analytics enables you to benefit from:

• Informed decision-making : You can make informed decisions based on actual data, which reduces uncertainty and improves outcomes.
• Business insights : Analyzing large datasets uncovers hidden patterns and trends, providing a deeper understanding of customer behavior and market dynamics.
• Customer understanding : Get insight into customer preferences and needs so you can personalize experiences and create more impactful marketing strategies.
• Operational efficiency : By analyzing operational data, you can optimize processes, identify bottlenecks, and streamline operations to reduce costs and improve productivity.
• Innovation : Big data analytics can help you uncover new opportunities and niches within industries. You can identify unmet needs and emerging trends to develop more innovative products and services to stay ahead of the competition.

There are four main types of big data analytics— descriptive , diagnostic , predictive , and prescriptive . Each serves a different purpose and offers varying levels of insight.

Collectively, they enable businesses to comprehensively understand their big data and make decisions to drive improved performance.

Let’s take a closer look at each one.

Descriptive analytics

This type focuses on summarizing historical data to tell youwhat’s happened in the past. It uses aggregation, data mining, and visualization techniques to understand trends, patterns, and key performance indicators (KPIs).

Descriptive analytics helps you understand your current situation and make informed decisions based on historical information.

Diagnostic analytics

Diagnostic analytics goes beyond describing past events and aims to understand why they occurred. It separates data to identify the root causes of specific outcomes or issues.

By analyzing relationships and correlations within the data, diagnostic analytics helps you gain insights into factors influencing your results.

Predictive analytics

This type of analytics uses historical data and statistical algorithms to predict future events. It spots patterns and trends and forecasts what might happen next.

You can use predictive analytics to anticipate customer behavior, product demand, market trends, and more to plan and make strategic decisions proactively.

Prescriptive analytics

Prescriptive analytics builds on predictive analytics by recommending actions to optimize future outcomes. It considers various possible actions and their potential impact on the predicted event or outcome.

Prescriptive analytics help you make data-driven decisions by suggesting the best course of action based on your desired goals and any constraints.

Descriptive Analytics - Diagnostic Analytics - Predictive Analytics - Prescriptive Analytics

Big data analytics has become a clear business game changer by unlocking insights and opportunities.

Below we’ve highlighted some of the tangible benefits of this transformative approach.

Improved risk management

Big data encompasses massive data volumes from diverse sources, including real-time streams. Rapid analysis helps detect anomalies or unusual patterns quickly, preventing risks like fraud or security breaches that can have widespread and immediate consequences.

Example : Banks use big data analytics to spot unusual spending patterns in real-time, helping prevent fraudulent credit card transactions and safeguarding customer accounts.

Cost-efficiency

Big data analytics can process and analyze extensive datasets, including handling large-scale data streams from sources like IoT devices or social media in real time.

This comprehensive analysis enables you to optimize your operations, identify inefficiencies, and reduce costs at a level that might not be achievable with smaller datasets.

Example : Big data analytics optimizes production in manufacturing by analyzing data from sensors on the factory floor, reducing downtime and minimizing maintenance costs.

Better decision making

Applying big data analytics provides deeper insights, as it can analyze diverse and unstructured data types.

You can use it to analyze everything from structured databases to unstructured text and multimedia content. This variety of data sources enables richer insights into customer behavior, market trends, and other critical factors, helping you make more informed and strategic decisions.

Example : An ecommerce platform uses big data analytics to analyze customer browsing behavior and purchase history. This enables personalized recommendations to help improve customer satisfaction and drive sales.

Deeper insights

Big data analytics extracts insights from vast and diverse datasets. This includes structured and unstructured data, making it better at revealing nuanced patterns and hidden relationships.

By delving into massive datasets, big data analytics can uncover insights that have a transformative impact on business strategies and operations.

Example : A healthcare provider uses big data analytics to explore patient data, clinical research, and external sources to find personalized treatment options for complex medical conditions.

Big data analytics has immense potential, but has its share of hurdles.

You may encounter some of these challenges, so it’s important to recognize and understand how to overcome them.

Here are a few to look out for.

Making data accessible and organized

Handling large and diverse datasets can make organizing and accessing information challenging.

We recommend a cohesive data infrastructure that enables easy retrieval and integration for practical analysis.

Maintaining quality

The sheer volume and variety of data can lead to inconsistencies and inaccuracies.

Ensuring data quality through cleaning, validation, and proper data governance helps prevent incorrect analysis and decision-making.

Keeping data secure

Maintaining data security is a major concern given the large volume of sensitive information collected and analyzed.

Safeguarding data against breaches, unauthorized access, and cyber threats protects customer privacy and business integrity.

Finding the right tools

The rapidly evolving landscape of big data tools and technologies can be overwhelming.

We recommend using a buying committee of internal stakeholders to evaluate tools that integrate well together and match your business needs and goals.

Real-world applications of big data analytics have ignited shifts and shaped approaches across several industries.

We’ll explore some examples and highlight how this methodology helps decision-making and innovation in many business sectors.

In healthcare, big data analytics processes vast volumes of patient records, medical images, and genomic data.

It identifies intricate patterns in large datasets to predict disease trends, enhance personalized treatments, and even anticipate potential outbreaks by analyzing global health data.

Product development

Big data analytics facilitates product development by analyzing structured data like sales records and unstructured data like customer reviews and social media interactions.

This enables companies to uncover hidden insights about customer preferences to produce more innovative and targeted products.

Media and entertainment

Big data analytics helps the media and entertainment industry by dissecting streams of viewership data and social media interactions.

These techniques unravel real-time trends, helping media companies rapidly adapt their content offerings, optimize ad placement, and personalize recommendations for diverse audiences.

Marketing companies can benefit from big data analytics in several ways. Unlike smaller-scale analytical approaches, it can analyze intricate customer behavior across various channels and dissect complex patterns in real time.

Marketers can offer highly personalized experiences, detect shifting trends faster, and responsively adjust their strategies.

Big data analytics in ecommerce is more than simple sales analysis. It dives into vast and diverse datasets, including clickstream data, purchase histories, and online interactions.

It enables real-time recommendations, dynamic pricing adjustments, and enhanced supply chain management for a seamless customer experience.

In the banking sector, big data analytics doesn’t only focus on transaction monitoring.

It processes enormous amounts of transaction data in real time, using advanced algorithms and machine learning to find unusual patterns and behavior. In doing so, big data analytics helps banks reduce false positives and provide more accurate fraud signals than other methods.

There are many techniques in the big data analytics toolbox and you'll likely come across many as you dissect and analyze your information.

If you’re looking for somewhere to start, these are foundational techniques for handling big data.

• Association Rule Learning: Used to find relationships or patterns in large datasets. It’s primarily applied in market basket analysis, where the goal is to discover associations between items frequently purchased together.
• Classification Tree Analysis : Used for predictive modeling and classification tasks. They partition the dataset into subsets based on input features and then assign a class label to each one. Decision trees are one type of classification tree.
• Genetic Algorithms : An optimization technique inspired by natural selection. This involves creating a population of potential solutions and evolving them over generations to find the best one. You can use genetic algorithms for various optimization problems, including feature selection, parameter tuning, etc.
• Machine Learning : This covers various techniques in which algorithms learn patterns from data and make predictions or decisions. It includes supervised learning where models are trained on labeled data, unsupervised learning where patterns are inferred from unlabeled data, and reinforcement learning where they learn to make decisions based on rewards or punishments.
• Clustering : An unsupervised learning technique in which data points are grouped into clusters based on similarity. It’s mostly used for customer segmentation and anomaly detection.
• Regression Analysis : Models the relationship between dependent and independent variables. It’s commonly used for predicting numerical values, such as sales based on advertising costs.
• Neural Networks : A class of machine learning models inspired by the brain’s structure. They consist of interconnected nodes—known as neurons—organized into layers. Deep learning is a subset of neural networks involving multiple hidden layers. Convolutional Neural Networks (CNNs) are used to analyze images, while Recurrent Neural Networks (RNNs) are used for sequence data.
• Text Mining and Natural Language Processing (NLP): Focused on processing and understanding human language, these are used for sentiment analysis, topic modeling, language generation, and more.
• Dimensionality Reduction : These techniques reduce the number of input features while preserving essential information. They help with visualization, noise reduction, and speeding up training.
• Time Series Analysis : Used to analyze data points collected over time for forecasting, anomaly detection, and trend analysis.

From healthcare to marketing, big data analytics offers a lens into the intricate workings of our interconnected world.

It empowers you to navigate complexities, spot trends that elude the naked eye, and transform data into actionable strategies that drive growth.

As the business landscape evolves, so does the scope and impact of big data analytics—this is where Amplitude Analytics can help.

Amplitude Analytics bridges the gap between raw data and meaningful insights, guiding you toward a deeper understanding of your user’s journey.

As an all-in-one data analytics platform, it applies all four types of big data analytics—predictive, prescriptive, descriptive, and diagnostic—-to help you garner insights across all areas of your business. You’ll be able to analyze your data and truly decipher the stories and potential it holds.

Enhance your product, engage your customers, and make data-backed decisions that resonate.

Get started with Amplitude Analytics today .

Other Analytics Guides

• What is Enterprise Analytics?
• What is Predictive Analytics?
• What is Diagnostic Analytics?
• What is Descriptive Analytics?
• What is Prescriptive Analytics?

Practice Exams

Course Notes

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Career Guides

A selection of practice exams that will test your current data science knowledge. Identify key areas of improvement to strengthen your theoretical preparation, critical thinking, and practical problem-solving skills so you can get one step closer to realizing your professional goals.

Excel Mechanics

Imagine if you had to apply the same Excel formatting adjustment to both Sheet 1 and Sheet 2 (i.e., adjust font, adjust fill color of the sheets, add a couple of empty rows here and there) which contain thousands of rows. That would cost an unjustifiable amount of time. That is where advanced Excel skills come in handy as they optimize your data cleaning, formatting and analysis process and shortcut your way to a job well-done. Therefore, asses your Excel data manipulation skills with this free practice exam.  

Formatting Excel Spreadsheets

Did you know that more than 1 in 8 people on the planet uses Excel and that Office users typically spend a third of their time in Excel. But how many of them use the popular spreadsheet tool efficiently? Find out where you stand in your Excel skills with this free practice exam where you are a first-year investment banking analyst at one of the top-tier banks in the world. The dynamic nature of your position will test your skills in quick Excel formatting and various Excel shortcuts 

Hypothesis Testing

Whenever we need to verify the results of a test or experiment we turn to hypothesis testing. In this free practice exam you are a data analyst at an electric car manufacturer, selling vehicles in the US and Canada. Currently the company offers two car models – Apollo and SpeedX.  You will need to download a free Excel file containing the car sales of the two models over the last 3 years in order find out interesting insights and  test your skills in hypothesis testing. 

Confidence Intervals

Confidence Intervals refers to the probability of a population parameter falling between a range of certain values. In this free practice exam, you lead the research team at a portfolio management company with over $50 billion dollars in total assets under management. You are asked to compare the performance of 3 funds with similar investment strategies  and are given a table with the return of the three portfolios over the last 3 years. You will have to use the data to answer questions that will test your knowledge in confidence intervals. 

Fundamentals of Inferential Statistics

While descriptive statistics helps us describe and summarize a dataset, inferential statistics allows us to make predictions based off data. In this free practice exam, you are a data analyst at a leading statistical research company. Much of your daily work relates to understanding data structures and processes, as well as applying analytical theory to real-world problems on large and dynamic datasets. You will be given an excel dataset and will be tested on normal distribution, standardizing a dataset, the Central Limit Theorem among other inferential statistics questions.   

Fundamentals of Descriptive Statistics

Descriptive statistics helps us understand the actual characteristics of a dataset by generating summaries about data samples. The most popular types of descriptive statistics are measures of center: median, mode and mean. In this free practice exam you have been appointed as a Junior Data Analyst at a property developer company in the US, where you are asked to evaluate the renting prices in 9 key states. You will work with a free excel dataset file that contains the rental prices and houses over the last years.

Jupyter Notebook Shortcuts

In this free practice exam you are an experienced university professor in Statistics who is looking to upskill in data science and has joined the data science apartment. As on of the most popular coding environments for Python, your colleagues recommend you learn Jupyter Notebook as a beginner data scientist. Therefore, in this quick assessment exam you are going to be tested on some basic theory regarding Jupyter Notebook and some of its shortcuts which will determine how efficient you are at using the environment. 

Intro to Jupyter Notebooks

Jupyter is a free, open-source interactive web-based computational notebook. As one of the most popular coding environments for Python and R, you are inevitably  going to encounter Jupyter at some point in you data science journey, if you have not already. Therefore, in this free practice exam you are a professor of Applied Economics and Finance who is learning how to use Jupyter. You are going to be tested on the very basics of the Jupyter environment like how to set up the environment and some Jupyter keyboard shortcuts. 

Black-Scholes-Merton Model in Python

The Black Scholes formula is one of the most popular financial instruments used in the past 40 years. Derived by Fisher, Black Myron Scholes and Robert Merton in 1973, it has become the primary tool for derivative pricing. In this free practice exam, you are a finance student whose Applied Finance is approaching and is asked to perform the Black-Scholes-Merton formula in Python  by working on a dataset containing Tesla’s stock prices for the period between mid-2010 and mid-2020.  

Python for Financial Analysis

In a heavily regulated industry like fintech, simplicity and efficiency is key. Which is why Python is the preferred choice for programming language over the likes of Java or C++. In this free practice exam you are a university professor of Applied Economics and Finance, who is focused on running regressions and applying the CAPM model on the NASDAQ and The Coca-Cola Company Dataset for the period between 2016 and 2020 inclusive. Make sure to have the following packages running to complete your practice test: pandas, numpy, api, scipy, and pyplot as plt. 

Python Finance

Python has become the ideal programming language for the financial industry, as more and more hedge funds and large investment banks are adopting this general multi-purpose language to solve their quantitative problems. In this free practice exam on Python Finance, you are part of the IT team of a huge company, operating in the US stock market, where you are asked to analyze the performance of three market indices. The packages you need to have running are numpy, pandas and pyplot as plt.   

Machine Learning with KNN

KNN is a popular supervised machine learning algorithm that is used for solving both classification and regression problems. In this free practice exam, this is exactly what you are going to be asked to do, as you are required to create 2 datasets for 2 car dealerships in Jupyter Notebook, fit the models to the training data, find the set of parameters that best classify a car, construct a confusion matrix and more.

Excel Functions

The majority of data comes in spreadsheet format, making Excel the #1 tool of choice for professional data analysts. The ability to work effectively and efficiently in Excel is highly desirable for any data practitioner who is looking to bring value to a company. As a matter of fact, being proficient in Excel has become the new standard, as 82% of middle-skill jobs require competent use of the productivity software. Take this free Excel Functions practice exam and test your knowledge on removing duplicate values, transferring data from one sheet to another, rand using the VLOOKUP and SUMIF function.

Useful Tools in Excel

What Excel lacks in data visualization tools compared to Tableau, or computational power for analyzing big data compared to Python, it compensates with accessibility and flexibility. Excel allows you to quickly organize, visualize and perform mathematical functions on a set of data, without the need for any programming or statistical skills. Therefore, it is in your best interest to learn how to use the various Excel tools at your disposal. This practice exam is a good opportunity to test your excel knowledge in the text to column functions, excel macros, row manipulation and basic math formulas.

Excel Basics

Ever since its first release in 1985, Excel continues to be the most popular spreadsheet application to this day- with approximately 750 million users worldwide, thanks to its flexibility and ease of use. No matter if you are a data scientist or not, knowing how to use Excel will greatly improve and optimize your workflow. Therefore, in this free Excel Basics practice exam you are going to work with a dataset of a company in the Fast Moving Consumer Goods Sector as an aspiring data analyst and test your knowledge on basic Excel functions and shortcuts.

A/B Testing for Social Media

In this free A/B Testing for Social Media practice exam, you are an experienced data analyst who works at a new social media company called FilmIt. You are tasked with the job of increasing user engagement by applying the correct modifications to how users move on to the next video. You decide that the best approach is by conducting a A/B test in a controlled environment. Therefore, in order to successfully complete this task, you are going to be tested on statistical significance, 2 tailed-tests and choosing the success metrics.

Fundamentals of A/B Testing

A/B Testing is a powerful statistical tool used to compare the results between two versions of the same marketing asset such as a webpage or email in a controlled environment. An example of A/B testing is when Electronic Arts created a variation version of the sales page for the popular SimCity 5 simulation game, which performed 40% better than the control page. Speaking about video games, in this free practice test, you are a data analyst who is tasked with the job to conduct A/B testing for a game developer. You are going to be asked to choose the best way to perform an A/B test, identify the null hypothesis, choose the right evaluation metrics, and ultimately increase revenue through in-game ads.

Introduction to Data Science Disciplines

The term “Data Science” dates back to the 1960s, to describe the emerging field of working with large amounts of data that drives organizational growth and decision-making. While the essence has remained the same, the data science disciplines have changed a lot over the past decades thanks to rapid technological advancements. In this free introduction to data science practice exam, you will test your understanding of the modern day data science disciplines and their role within an organization.

Advanced SQL

In this free Advanced SQL practice exam you are a sophomore Business student who has decided to focus on improving your coding and analytical skills in the areas of relational database management systems. You are given an employee dataset containing information like titles, salaries, birth dates and department names, and are required to come up with the correct answers. This free SQL practice test will evaluate your knowledge on MySQL aggregate functions , DML statements (INSERT, UPDATE) and other advanced SQL queries.

Most Popular Practice Exams

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What is Big Data Analytics? Full Guide + How Businesses Can Use It

Explore the transformative world of big data analytics and discover how Segment empowers businesses to unlock actionable insights from large datasets.

What is big data analytics?

Key components of big data analytics, practical applications of big data analytics, best practices in big data analytics, simplify big data analytics with segment.

Big data refers to large data sets that are difficult to manage due to their volume, velocity, and variety. But the complexity and scale of big data is also the reason for its potential: analyzing these data sets can unlock greater insights, better operational efficiency, and higher revenue growth. 

Big data analytics means processing large volumes of raw data to extract insights on user behavior, create data visualizations, and understand market trends. While this sounds like a straightforward process, the reality is that a business will struggle to glean any valuable insights without a proper big data infrastructure .

Data analytics has a long history , from manual statistical analysis to the invention of relational databases to manage structured data. As businesses began collecting a greater variety of data, non-relational databases (NoSQL) emerged as a solution for unstructured data.

Today, distributed processing technologies (Apache Hadoop, NoSQL databases, massively parallel processing) help companies build a scalable big data infrastructure that supports high-speed, and often real-time data processing. 

Analyzing big data means accounting for its volume, velocity, and variety. Organizations need to handle the ingestion, processing, and storage of large data sets at scale, which can often include a variety of different data types (including structured, unstructured, and semi-structured ). 

The majority of companies manage between one and five petabytes of data, according to a recent survey . These large amounts of data stream in from diverse sources – Internet of Things devices, payment equipment, social media, web apps, and more.

Almost four in five data experts report that the speed of data collection has outpaced their ability to extract value from their data, especially if most of the data is siloed.

Scalable data ingestion is necessary to forward all of this data to a repository (like a data warehouse or lake). From here, you can transform this data and share it with analytics tools. For example, Twilio Segment’s customer data platform (CDP) gathers data from fragmented sources, such as mobile, web, and the cloud. Then, it automatically transforms the data according to your data quality standards and uploads it to a database where it’s ready for analytics.

Velocity refers to the speed of data generation. Big data is generated in real time (or near real time), so your ingestion engine must handle a constant data stream. This is important for big data analytics tools that rely on real-time data, like fraud prevention solutions.

Consider Camping World , a business that specializes in RVs. They used Twilio Segment’s real-time data collection capabilities to personalize customer interactions, resulting in a 12% increase in conversion rates.

Big data means diverse data: semi-structured, unstructured, and structured. Additionally, different teams may use different formats and naming conventions, which can degrade the quality of data (e.g., logging duplicate entries). 

For example, one team may use the nomenclature “Signed_Up” while another uses “sign_up.” This is the same event, but since it’s named differently, it’s tracked as two separate ones.  

Companies across industries use different types of analytics to transform their big data into fuel for decision-making. Here’s a look at how this is done in industries like finance, healthcare, marketing, and cybersecurity. 

The finance sector applies big data analytics to manage risk, automate investing, and detect fraud – among many other applications.

Machine learning models trained on various financial data can analyze the creditworthiness of a person or business. So, instead of relying on the credit score for risk assessment, a lender gets a more complete picture of an applicant’s ability to repay. Using these predictive insights, the lender may expand their customer base and generate more revenue without extra risk.

Big data analytics also allows robo-advisors (automated investing services) to make investments based on the client’s preferences. This has made investing accessible to anyone, not just people with a high net worth.

In fraud prevention, predictive analytics uses historical data to flag suspicious activity in real time. These solutions not only protect consumers but also allow financial service providers to save resources they would’ve spent on fraud investigations.

Big data supports predictive analytics in healthcare , allowing healthcare providers to maximize the use of existing resources. For example, predictive algorithms can forecast patient demand for a given period. If the algorithm predicts a patient surge, a hospital has enough time to allocate its resources and prevent staff overwhelm.

Access to big data analytics improves patient care in many ways – from preventing the development of chronic illness to detecting disease in its early stages. Healthcare practitioners can even develop personalized care plans for patients based on predictions of how they will respond to their treatment.

Without big data analytics, it wouldn’t be possible to deliver the high degree of personalization that consumers now expect. Sixty-two percent of business leaders say that personalization boosts retention, according to a Segment report , and businesses are setting aside more of their budget for this purpose. 

Big data tools enable marketing teams to understand what turns a website visitor into a customer and what kind of interactions retain them. 

Big data helps a business analyze which products attract specific customer segments, which allows the marketing team to target just the right audience. Data also uncovers what frustrates consumers and why they drop out of the sales funnel.

Learn more: Why Business Data is the Key to Unlocking Growth

Cybersecurity

Cybersecurity teams leverage big data to improve threat detection and prevent data breaches. 

Consider an employee whose credentials have been stolen by a threat actor, giving them full access to sensitive company information. With user and entity behavior analytics (a big data-powered cybersecurity solution), cybersecurity teams can quickly detect suspicious behavior and take action to contain the threat. 

Typically, detecting an insider threat takes an average of 85 days . However, analyzing big data allows cybersecurity teams to determine a baseline for non-suspicious user behavior, which illuminates suspicious actions in real time.

Big data is complex, so it’s easy to make mistakes that lower the value of your data. Making sure it’s analytics-ready means you’ll need to strategically approach data quality, infrastructure scalability, security, and compliance.

Data quality and consistency

Regardless of what kind of analytics you’re running, you need reliable data to produce consistent and high-quality results. Reliable data is complete and accurate. But a big data system can’t produce it by default. It needs tools and processes that constantly clean and validate data. 

A major risk of poor-quality data is basing important business decisions on incomplete information. Additionally, you would lose out on all of the benefits of reliable data, including improved customer loyalty, faster product development, and revenue growth.

Scalability and efficiency

A big data system must be able to adapt to growing data volumes without hurting query performance. Building such data infrastructure from scratch is a resource-intensive process, so many businesses opt for a third-party solution to save time. 

As an example, take Retool , a platform for creating business apps. They used Twilio Segment to unify their customer data and scale their infrastructure, which saved them over 1,000 engineering hours per year. With Segment’s efficiency, everyone (from finance to product teams) could pull the data they need to understand user behavior and improve their experience.

Data security and compliance

Big data systems are a prime target for threat actors looking to steal sensitive data and cause damage to your business operations. Therefore, your data security measures should include encryption (in transit and at rest), regular penetration testing, and robust access controls to your data system.

Compliance with the CCPA, GDPR, and other privacy regulations is another priority. Consumers are becoming more aware of their data rights – the Twilio Segment platform saw a 69% increase in user deletion requests in 2022. Businesses must be able to easily comply with such requests and do so at scale, which is why automation is an ideal solution in a big data system.

Building a compliant and secure big data system is difficult and requires engineering resources that you could’ve spent on your product. As a result, many companies go for platforms that take care of security and compliance for them.

Twilio Segment’s CDP helps businesses in healthcare, finance, retail, and many other industries run big data analytics effectively and at scale.

Connections

Connections is Segment’s product for data unification. With just one API, Connections gathers first-party data from various sources and unifies it into a central hub. With the data in one place, you’ll get a complete customer view and an opportunity to analyze their behavior in-depth. You can then send this unified data to other business apps, such as marketing automation tools.

Protocols is Segment’s data quality solution. It allows you to create a tracking plan for automatic data validation upon ingestion, so any issues are captured before they leave an impact on your analytics. You can quarantine any events that don’t conform to your tracking plan for later review. With Protocols, companies like Typeform improved their data governance and reduced the number of tracked events by 75%.

Interested in hearing more about how Segment can help you?

Connect with a Segment expert who can share more about what Segment can do for you.

Frequently asked questions

How does segment enhance big data analytics capabilities, what future trends can we expect in big data analytics, what are the benefits of big data analytics, how does big data analytics drive customer acquisition and retention.

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Introduction to Big Data with Spark and Hadoop

This course is part of multiple programs. Learn more

This course is part of multiple programs

Taught in English

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Instructors: Aije Egwaikhide +2 more

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Intermediate level

Data literacy, Python, and SQL knowledge will be beneficial.

What you'll learn

Explain the impact of big data, including use cases, tools, and processing methods.

Describe Apache Hadoop architecture, ecosystem, practices, and user-related applications, including Hive, HDFS, HBase, Spark, and MapReduce.

Apply Spark programming basics, including parallel programming basics for DataFrames, data sets, and Spark SQL.

Use Spark’s RDDs and data sets, optimize Spark SQL using Catalyst and Tungsten, and use Spark’s development and runtime environment options.

Skills you'll gain

• Apache Hadoop

Apache Spark

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There are 7 modules in this course

This self-paced IBM course will teach you all about big data! You will become familiar with the characteristics of big data and its application in big data analytics. You will also gain hands-on experience with big data processing tools like Apache Hadoop and Apache Spark.

Bernard Marr defines big data as the digital trace that we are generating in this digital era. You will start the course by understanding what big data is and exploring how insights from big data can be harnessed for a variety of use cases. You’ll also explore how big data uses technologies like parallel processing, scaling, and data parallelism. Next, you will learn about Hadoop, an open-source framework that allows for the distributed processing of large data and its ecosystem. You will discover important applications that go hand in hand with Hadoop, like Distributed File System (HDFS), MapReduce, and HBase. You will become familiar with Hive, a data warehouse software that provides an SQL-like interface to efficiently query and manipulate large data sets. You’ll then gain insights into Apache Spark, an open-source processing engine that provides users with new ways to store and use big data. In this course, you will discover how to leverage Spark to deliver reliable insights. The course provides an overview of the platform, going into the components that make up Apache Spark. You’ll learn about DataFrames and perform basic DataFrame operations and work with SparkSQL. Explore how Spark processes and monitors the requests your application submits and how you can track work using the Spark Application UI. This course has several hands-on labs to help you apply and practice the concepts you learn. You will complete Hadoop and Spark labs using various tools and technologies, including Docker, Kubernetes, Python, and Jupyter Notebooks.

What Is Big Data?

In this module, you’ll begin your acquisition of Big Data knowledge with the most up-to-date definition of Big Data. You’ll explore the impact of Big Data on everyday personal tasks and business transactions with Big Data Use Cases. You’ll also learn how Big Data uses parallel processing, scaling, and data parallelism. Going further, you’ll explore commonly used Big Data tools and explain the role of open-source in Big Data. Finally, you’ll go beyond the hype and explore additional Big Data viewpoints.

What's included

8 videos 1 reading 2 assignments 1 plugin

8 videos • Total 47 minutes

• Course Introduction • 5 minutes • Preview module
• What is Big Data? • 7 minutes
• Impact of Big Data • 5 minutes
• Parallel Processing, Scaling, and Data Parallelism • 7 minutes
• Big Data Tools and Ecosystem • 4 minutes
• Open Source and Big Data • 6 minutes
• Beyond the Hype • 4 minutes
• Big Data Use Cases • 5 minutes

1 reading • Total 2 minutes

• Summary and Highlights: Introduction to Big Data • 2 minutes

2 assignments • Total 41 minutes

• Practice Quiz: Introduction to Big Data • 14 minutes
• Graded Quiz: What Is Big Data? • 27 minutes

1 plugin • Total 12 minutes

• Module 1 Glossary: What Is Big Data? • 12 minutes

Introduction to the Hadoop Ecosystem

In this module, you'll gain a fundamental understanding of the Apache Hadoop architecture, ecosystem, practices, and commonly used applications, including Distributed File System (HDFS), MapReduce, Hive, and HBase. You’ll also gain practical skills in hands-on labs when you query the data added using Hive, launch a single-node Hadoop cluster using Docker, and run MapReduce jobs.

6 videos 1 reading 2 assignments 3 app items 2 plugins

6 videos • Total 37 minutes

• Introduction to Hadoop • 7 minutes • Preview module
• Intro to MapReduce • 5 minutes
• Hadoop Ecosystem • 4 minutes
• HDFS • 8 minutes
• HIVE • 5 minutes
• HBASE • 5 minutes
• Summary and Highlights: Introduction to Hadoop • 2 minutes

2 assignments • Total 36 minutes

• Practice Quiz: Introduction to Hadoop • 12 minutes
• Graded Quiz: Introduction to Hadoop Ecosystem • 24 minutes

3 app items • Total 60 minutes

• Hands-on Lab: Getting Started with Hive • 20 minutes
• Hands-on Lab: Hadoop MapReduce • 20 minutes
• Hands-on lab : Hadoop Cluster (Optional) • 20 minutes

2 plugins • Total 30 minutes

• Cheat Sheet: Introduction to the Hadoop Ecosystem • 15 minutes
• Module 2 Glossary: Introduction to the Hadoop Ecosystem • 15 minutes

In this module, you’ll turn your attention to the popular Apache Spark platform, where you will explore the attributes and benefits of Apache Spark and distributed computing. You'll gain key insights about functional programming and Lambda functions. You’ll also explore Resilient Distributed Datasets (RDDs), parallel programming, resilience in Apache Spark, and relate RDDs and parallel programming with Apache Spark. Then, you’ll dive into additional Apache Spark components and learn how Apache Spark scales with Big Data. Working with Big Data signals the need for working with queries, including structured queries using SQL. You’ll also learn about the functions, parts, and benefits of Spark SQL and DataFrame queries, and discover how DataFrames work with Spark SQL.

5 videos 1 reading 2 assignments 1 app item 2 plugins

5 videos • Total 24 minutes

• Why use Apache Spark? • 5 minutes • Preview module
• Functional Programming Basics • 5 minutes
• Parallel Programming using Resilient Distributed Datasets • 5 minutes
• Scale out / Data Parallelism in Apache Spark • 3 minutes
• Dataframes and SparkSQL • 4 minutes
• Summary and Highlights: Introduction to Apache Spark • 2 minutes

2 assignments • Total 31 minutes

• Practice Quiz: Introduction to Apache Spark • 10 minutes
• Graded Quiz: Apache Spark • 21 minutes

1 app item • Total 15 minutes

• Hands-on Lab: Getting Started with Spark using Python • 15 minutes
• Cheat Sheet: Apache Spark • 15 minutes
• Module 3 Glossary: Apache Spark • 15 minutes

DataFrames and Spark SQL

In this module, you’ll learn about Resilient Distributed Datasets (RDDs), their uses in Apache Spark, and RDD transformations and actions. You'll compare the use of datasets with Spark's latest data abstraction, DataFrames. You'll learn to identify and apply basic DataFrame operations. You’ll explore Apache Spark SQL optimization and learn how Spark SQL and memory optimization benefit from using Catalyst and Tungsten. Finally, you’ll fortify your skills with guided hands-on lab to create a table view and apply data aggregation techniques.

5 videos 1 reading 2 assignments 2 app items 4 plugins

5 videos • Total 25 minutes

• RDDs in Parallel Programming and Spark • 5 minutes • Preview module
• Data-frames and Datasets • 4 minutes
• Catalyst and Tungsten • 5 minutes
• ETL with DataFrames • 6 minutes
• Real-world usage of SparkSQL • 4 minutes
• Summary and Highlights: Introduction to DataFrames and Spark SQL • 2 minutes
• Practice Quiz: Introduction to DataFrames & Spark SQL • 10 minutes
• Graded Quiz: DataFrames and Spark SQL • 21 minutes

2 app items • Total 30 minutes

• Hands-on Lab: Introduction to DataFrames • 15 minutes
• Hands-On Lab: Introduction to SparkSQL • 15 minutes

4 plugins • Total 60 minutes

• Reading: User-Defined Schema (UDS) for DSL and SQL • 10 minutes
• Reading: Common Transformations and Optimization Techniques in Spark • 20 minutes
• Cheat Sheet: DataFrames and Spark SQL • 15 minutes
• Module 4 Glossary: DataFrames and Spark SQL • 15 minutes

Development and Runtime Environment Options

In this module, you’ll explore how Spark processes the requests that your application submits and learn how you can track work using the Spark Application UI. Because Spark application work happens on the cluster, you need to be able to identify Apache Cluster Managers, their components, and benefits. You’ll also know how to connect with each cluster manager and how and when you might want to set up a local, standalone Spark instance. Next, you’ll learn about Apache Spark application submission, including the use of Spark’s unified interface, “spark-submit,” and learn about options and dependencies. You’ll also describe and apply options for submitting applications, identify external application dependency management techniques, and list Spark Shell benefits. You’ll also look at recommended practices for Spark's static and dynamic configuration options and perform hands-on labs to use Apache Spark on IBM Cloud and run Spark on Kubernetes.

6 videos 2 readings 3 assignments 2 app items 4 plugins

6 videos • Total 32 minutes

• Apache Spark Architecture • 5 minutes • Preview module
• Overview of Apache Spark Cluster Modes • 6 minutes
• How to Run an Apache Spark Application • 6 minutes
• Using Apache Spark on IBM Cloud • 4 minutes
• Setting Apache Spark Configuration • 5 minutes
• Running Spark on Kubernetes • 4 minutes

2 readings • Total 4 minutes

• Summary and Highlights: Spark Architecture • 2 minutes
• Summary and Highlights: Spark Runtime Environments • 2 minutes

3 assignments • Total 33 minutes

• Practice Quiz: Spark Architecture • 6 minutes
• Practice Quiz: Spark Runtime Environments • 6 minutes
• Graded Quiz: Development and Runtime Environment Options • 21 minutes

2 app items • Total 80 minutes

• Hands-on Lab: Submit Apache Spark Applications • 60 minutes
• Hands-on Lab: Apache Spark on Kubernetes • 20 minutes

4 plugins • Total 40 minutes

• Spark Environments - Overview and Options • 5 minutes
• How to Set Up Your Own Spark Environments (Optional) • 5 minutes
• Cheat Sheet: Development and Runtime Environment Options • 15 minutes
• Module 5 Glossary: Development and Runtime Environment Options • 15 minutes

Monitoring and Tuning

Platforms and applications require monitoring and tuning to manage issues that inevitably happen. In this module, you'll learn about connecting the Apache Spark user interface web server and using the same UI web server to manage application processes. You’ll also identify common Apache Spark application issues and learn about debugging issues using the application UI and locating related log files. Further, you’ll discover and gain real-world knowledge about how Spark manages memory and processor resources using the hands-on lab.

5 videos 1 reading 2 assignments 1 app item 3 plugins

5 videos • Total 30 minutes

• The Apache Spark User Interface • 5 minutes • Preview module
• Monitoring Application Progress • 7 minutes
• Debugging Apache Spark Application Issues • 5 minutes
• Understanding Memory Resources • 5 minutes
• Understanding Processor Resources • 5 minutes
• Summary and Highlights: Introduction to Monitoring and Tuning • 2 minutes
• Practice Quiz: Introduction to Monitoring and Tuning • 10 minutes
• Graded Quiz: Monitoring and Tuning • 21 minutes

1 app item • Total 30 minutes

• Hands-on Lab: Monitoring and Performance Tuning • 30 minutes

3 plugins • Total 35 minutes

• [Optional] Batch Data Ingestion Methods • 5 minutes
• Cheat Sheet: Monitoring and Tuning • 15 minutes
• Module 6 Glossary: Monitoring and Tuning • 15 minutes

Final Project and Assessment

In this module, you’ll perform a practice lab where you’ll explore two critical aspects of data processing using Spark: working with Resilient Distributed Datasets (RDDs) and constructing DataFrames from JSON data. You will also apply various transformations and actions on both RDDs and DataFrames to gain insights and manipulate the data effectively. Further, you’ll apply your knowledge in a final project where you will create a DataFrame by loading data from a CSV file and applying transformations and actions using Spark SQL. Finally, you’ll be assessed based on your learning from the course.

3 readings 1 assignment 2 app items 2 plugins

3 readings • Total 5 minutes

• Instructions for the Final Assessment • 1 minute
• Congratulations and Next Steps • 2 minutes
• Thanks from the Course Team • 2 minutes

1 assignment • Total 100 minutes

• Final Assessment • 100 minutes

2 app items • Total 120 minutes

• Practice Project: Data Processing Using Spark • 60 minutes
• Final Project: Data Analysis using Spark • 60 minutes

2 plugins • Total 35 minutes

• Final Project Overview • 15 minutes
• Glossary: Introduction to Big Data with Spark and Hadoop • 20 minutes

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world. For more information about IBM visit: www.ibm.com

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337 reviews

Reviewed on May 2, 2022

hands on lab and quizzes at the end of each session was very helpful

Reviewed on May 8, 2022

Fantastic blend of theory and practical (labs). The labs are short and have concise material.

Reviewed on Nov 12, 2022

This is really helpful for me to understand Big Data and Apache Spark!

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Big Data Analytics Tutorial

• Big Data Analytics - Home
• Big Data Analytics - Fundamentals
• Big Data Analytics - Hadoop Ecosystem & Its Components
• Big Data Analytics - Hadoop & Components
• Big Data Analytics - Descriptive Analytics
• Big Data Analytics - Prescriptive Analytics
• Big Data Analytics – Big Data Stack
• Big Data Analytics - Importance of Predictive Analytics
• Big Data Analytics – 7 V's of Big Data
• Big Data Analytics - Big Data Architecture
• Big Data Analytics - Hypervisor
• Big Data Analytics - Operational Database
• Big Data Analytics - Key-value Database
• Big Data Analytics - Big Data Types
• Big Data Analytics - Big Data Adoption & Planning Considerations
• Big Data Analytics - Data Abstraction
• Big Data Analytics - Big Data Analytics Life Cycle
• Big Data Analytics - Challenges
• Big Data Analytics - MapReduce
• Big Data Analytics - How MapReduce Works?
• Big Data Analytics - HDFS Architecture
• Big Data Analytics - Hadoop YARN Architecture
• Getting Started with Data Analytics using AWS Glue
• Apache Kafkas

Big Data Analytics MCQs

• Big Data Analytics - MCQs Home
• MCQs | Big Data Analytics – Fundamentals
• MCQs | Big Data Analytics – Hadoop Introduction, Ecosystem and Its Components
• MCQs | Big Data Analytics – Descriptive Analytics
• MCQs | Big Data Analytics – Prescriptive Analytics
• MCQs | Big Data Analytics – Big Data Stack
• MCQs | Big Data Analytics – Predictive Analytics
• MCQs | Big Data Analytics – 7 V's of Big Data
• MCQs | Big Data Analytics – Big Data Architecture
• MCQs | Big Data Analytics – Hypervisor
• MCQs | Big Data Analytics – Operational Database

Home » Big Data Analytics

Big Data Analytics Multiple-Choice Questions (MCQs)

Big Data Analytics MCQs : This section contains multiple-choice questions and answers on the various topics of Big Data Analytics such as fundamentals, Hadoop introduction, descriptive analytics, prescriptive analytics, big data stack, 7 V's of big data, big data structure, hypervisor, operational database, etc.

These MCQs on Big Data Analytics are specially designed for professionals and students to test and enhance their skills. Practice these MCQs to prepare well for the exams.

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CSE 163, Summer 2020: Homework 3: Data Analysis

In this assignment, you will apply what you've learned so far in a more extensive "real-world" dataset using more powerful features of the Pandas library. As in HW2, this dataset is provided in CSV format. We have cleaned up the data some, but you will need to handle more edge cases common to real-world datasets, including null cells to represent unknown information.

Note that there is no graded testing portion of this assignment. We still recommend writing tests to verify the correctness of the methods that you write in Part 0, but it will be difficult to write tests for Part 1 and 2. We've provided tips in those sections to help you gain confidence about the correctness of your solutions without writing formal test functions!

This assignment is supposed to introduce you to various parts of the data science process involving being able to answer questions about your data, how to visualize your data, and how to use your data to make predictions for new data. To help prepare for your final project, this assignment has been designed to be wide in scope so you can get practice with many different aspects of data analysis. While this assignment might look large because there are many parts, each individual part is relatively small.

Learning Objectives

After this homework, students will be able to:

• Work with basic Python data structures.
• Handle edge cases appropriately, including addressing missing values/data.
• Practice user-friendly error-handling.
• Read plotting library documentation and use example plotting code to figure out how to create more complex Seaborn plots.
• Train a machine learning model and use it to make a prediction about the future using the scikit-learn library.

Expectations

Here are some baseline expectations we expect you to meet:

Follow the course collaboration policies

If you are developing on Ed, all the files are there. The files included are:

• hw3-nces-ed-attainment.csv : A CSV file that contains data from the National Center for Education Statistics. This is described in more detail below.
• hw3.py : The file for you to put solutions to Part 0, Part 1, and Part 2. You are required to add a main method that parses the provided dataset and calls all of the functions you are to write for this homework.
• hw3-written.txt : The file for you to put your answers to the questions in Part 3.
• cse163_utils.py : Provides utility functions for this assignment. You probably don't need to use anything inside this file except importing it if you have a Mac (see comment in hw3.py )

If you are developing locally, you should navigate to Ed and in the assignment view open the file explorer (on the left). Once there, you can right-click to select the option to "Download All" to download a zip and open it as the project in Visual Studio Code.

The dataset you will be processing comes from the National Center for Education Statistics. You can find the original dataset here . We have cleaned it a bit to make it easier to process in the context of this assignment. You must use our provided CSV file in this assignment.

The original dataset is titled: Percentage of persons 25 to 29 years old with selected levels of educational attainment, by race/ethnicity and sex: Selected years, 1920 through 2018 . The cleaned version you will be working with has columns for Year, Sex, Educational Attainment, and race/ethnicity categories considered in the dataset. Note that not all columns will have data starting at 1920.

Our provided hw3-nces-ed-attainment.csv looks like: (⋮ represents omitted rows):

Column Descriptions

• Year: The year this row represents. Note there may be more than one row for the same year to show the percent breakdowns by sex.
• Sex: The sex of the students this row pertains to, one of "F" for female, "M" for male, or "A" for all students.
• Min degree: The degree this row pertains to. One of "high school", "associate's", "bachelor's", or "master's".
• Total: The total percent of students of the specified gender to reach at least the minimum level of educational attainment in this year.
• White / Black / Hispanic / Asian / Pacific Islander / American Indian or Alaska Native / Two or more races: The percent of students of this race and the specified gender to reach at least the minimum level of educational attainment in this year.

Interactive Development

When using data science libraries like pandas , seaborn , or scikit-learn it's extremely helpful to actually interact with the tools your using so you can have a better idea about the shape of your data. The preferred practice by people in industry is to use a Jupyter Notebook, like we have been in lecture, to play around with the dataset to help figure out how to answer the questions you want to answer. This is incredibly helpful when you're first learning a tool as you can actually experiment and get real-time feedback if the code you wrote does what you want.

We recommend that you try figuring out how to solve these problems in a Jupyter Notebook so you can actually interact with the data. We have made a Playground Jupyter Notebook for you that has the data uploaded. At the top-right of this page in Ed is a "Fork" button (looks like a fork in the road). This will make your own copy of this Notebook so you can run the code and experiment with anything there! When you open the Workspace, you should see a list of notebooks and CSV files. You can always access this launch page by clicking the Jupyter logo.

Part 0: Statistical Functions with Pandas

In this part of the homework, you will write code to perform various analytical operations on data parsed from a file.

Part 0 Expectations

• All functions for this part of the assignment should be written in hw3.py .
• For this part of the assignment, you may import and use the math and pandas modules, but you may not use any other imports to solve these problems.
• For all of the problems below, you should not use ANY loops or list/dictionary comprehensions. The goal of this part of the assignment is to use pandas as a tool to help answer questions about your dataset.

Problem 0: Parse data

In your main method, parse the data from the CSV file using pandas. Note that the file uses '---' as the entry to represent missing data. You do NOT need to anything fancy like set a datetime index.

The function to read a CSV file in pandas takes a parameter called na_values that takes a str to specify which values are NaN values in the file. It will replace all occurrences of those characters with NaN. You should specify this parameter to make sure the data parses correctly.

Problem 1: compare_bachelors_1980

What were the percentages for women vs. men having earned a Bachelor's Degree in 1980? Call this method compare_bachelors_1980 and return the result as a DataFrame with a row for men and a row for women with the columns "Sex" and "Total".

The index of the DataFrame is shown as the left-most column above.

Problem 2: top_2_2000s

What were the two most commonly awarded levels of educational attainment awarded between 2000-2010 (inclusive)? Use the mean percent over the years to compare the education levels in order to find the two largest. For this computation, you should use the rows for the 'A' sex. Call this method top_2_2000s and return a Series with the top two values (the index should be the degree names and the values should be the percent).

For example, assuming we have parsed hw3-nces-ed-attainment.csv and stored it in a variable called data , then top_2_2000s(data) will return the following Series (shows the index on the left, then the value on the right)

Hint: The Series class also has a method nlargest that behaves similarly to the one for the DataFrame , but does not take a column parameter (as Series objects don't have columns).

Our assert_equals only checks that floating point numbers are within 0.001 of each other, so your floats do not have to match exactly.

Optional: Why 0.001?

Whenever you work with floating point numbers, it is very likely you will run into imprecision of floating point arithmetic . You have probably run into this with your every day calculator! If you take 1, divide by 3, and then multiply by 3 again you could get something like 0.99999999 instead of 1 like you would expect.

This is due to the fact that there is only a finite number of bits to represent floats so we will at some point lose some precision. Below, we show some example Python expressions that give imprecise results.

Because of this, you can never safely check if one float is == to another. Instead, we only check that the numbers match within some small delta that is permissible by the application. We kind of arbitrarily chose 0.001, and if you need really high accuracy you would want to only allow for smaller deviations, but equality is never guaranteed.

Problem 3: percent_change_bachelors_2000s

What is the difference between total percent of bachelor's degrees received in 2000 as compared to 2010? Take a sex parameter so the client can specify 'M', 'F', or 'A' for evaluating. If a call does not specify the sex to evaluate, you should evaluate the percent change for all students (sex = ‘A’). Call this method percent_change_bachelors_2000s and return the difference (the percent in 2010 minus the percent in 2000) as a float.

For example, assuming we have parsed hw3-nces-ed-attainment.csv and stored it in a variable called data , then the call percent_change_bachelors_2000s(data) will return 2.599999999999998 . Our assert_equals only checks that floating point numbers are within 0.001 of each other, so your floats do not have to match exactly.

Hint: For this problem you will need to use the squeeze() function on a Series to get a single value from a Series of length 1.

Part 1: Plotting with Seaborn

Next, you will write functions to generate data visualizations using the Seaborn library. For each of the functions save the generated graph with the specified name. These methods should only take the pandas DataFrame as a parameter. For each problem, only drop rows that have missing data in the columns that are necessary for plotting that problem ( do not drop any additional rows ).

Part 1 Expectations

• When submitting on Ed, you DO NOT need to specify the absolute path (e.g. /home/FILE_NAME ) for the output file name. If you specify absolute paths for this assignment your code will not pass the tests!
• You will want to pass the parameter value bbox_inches='tight' to the call to savefig to make sure edges of the image look correct!
• For this part of the assignment, you may import the math , pandas , seaborn , and matplotlib modules, but you may not use any other imports to solve these problems.
• For all of the problems below, you should not use ANY loops or list/dictionary comprehensions.
• Do not use any of the other seaborn plotting functions for this assignment besides the ones we showed in the reference box below. For example, even though the documentation for relplot links to another method called scatterplot , you should not call scatterplot . Instead use relplot(..., kind='scatter') like we showed in class. This is not an issue of stylistic preference, but these functions behave slightly differently. If you use these other functions, your output might look different than the expected picture. You don't yet have the tools necessary to use scatterplot correctly! We will see these extra tools later in the quarter.

Part 1 Development Strategy

• Print your filtered DataFrame before creating the graph to ensure you’re selecting the correct data.
• Call the DataFrame describe() method to see some statistical information about the data you've selected. This can sometimes help you determine what to expect in your generated graph.
• Re-read the problem statement to make sure your generated graph is answering the correct question.
• Compare the data on your graph to the values in hw3-nces-ed-attainment.csv. For example, for problem 0 you could check that the generated line goes through the point (2005, 28.8) because of this row in the dataset: 2005,A,bachelor's,28.8,34.5,17.6,11.2,62.1,17.0,16.4,28.0

Seaborn Reference

Of all the libraries we will learn this quarter, Seaborn is by far the best documented. We want to give you experience reading real world documentation to learn how to use a library so we will not be providing a specialized cheat-sheet for this assignment. What we will do to make sure you don't have to look through pages and pages of documentation is link you to some key pages you might find helpful for this assignment; you do not have to use every page we link, so part of the challenge here is figuring out which of these pages you need. As a data scientist, a huge part of solving a problem is learning how to skim lots of documentation for a tool that you might be able to leverage to solve your problem.

We recommend to read the documentation in the following order:

• Start by skimming the examples to see the possible things the function can do. Don't spend too much time trying to figure out what the code is doing yet, but you can quickly look at it to see how much work is involved.
• Then read the top paragraph(s) that give a general overview of what the function does.
• Now that you have a better idea of what the function is doing, go look back at the examples and look at the code much more carefully. When you see an example like the one you want to generate, look carefully at the parameters it passes and go check the parameter list near the top for documentation on those parameters.
• It sometimes (but not always), helps to skim the other parameters in the list just so you have an idea what this function is capable of doing

As a reminder, you will want to refer to the lecture/section material to see the additional matplotlib calls you might need in order to display/save the plots. You'll also need to call the set function on seaborn to get everything set up initially.

Here are the seaborn functions you might need for this assignment:

• Bar/Violin Plot ( catplot )
• Plot a Discrete Distribution ( distplot ) or Continuous Distribution ( kdeplot )
• Scatter/Line Plot ( relplot )
• Linear Regression Plot ( regplot )
• Compare Two Variables ( jointplot )
• Heatmap ( heatmap )

Make sure you read the bullet point at the top of the page warning you to only use these functions!

Problem 0: Line Chart

Plot the total percentages of all people of bachelor's degree as minimal completion with a line chart over years. To select all people, you should filter to rows where sex is 'A'. Label the x-axis "Year", the y-axis "Percentage", and title the plot "Percentage Earning Bachelor's over Time". Name your method line_plot_bachelors and save your generated graph as line_plot_bachelors.png .

Problem 1: Bar Chart

Plot the total percentages of women, men, and total people with a minimum education of high school degrees in the year 2009. Label the x-axis "Sex", the y-axis "Percentage", and title the plot "Percentage Completed High School by Sex". Name your method bar_chart_high_school and save your generated graph as bar_chart_high_school.png .

Do you think this bar chart is an effective data visualization? Include your reasoning in hw3-written.txt as described in Part 3.

Problem 2: Custom Plot

Plot the results of how the percent of Hispanic individuals with degrees has changed between 1990 and 2010 (inclusive) for high school and bachelor's degrees with a chart of your choice. Make sure you label your axes with descriptive names and give a title to the graph. Name your method plot_hispanic_min_degree and save your visualization as plot_hispanic_min_degree.png .

Include a justification of your choice of data visualization in hw3-written.txt , as described in Part 3.

Part 2: Machine Learning using scikit-learn

Now you will be making a simple machine learning model for the provided education data using scikit-learn . Complete this in a function called fit_and_predict_degrees that takes the data as a parameter and returns the test mean squared error as a float. This may sound like a lot, so we've broken it down into steps for you:

• Filter the DataFrame to only include the columns for year, degree type, sex, and total.
• Do the following pre-processing: Drop rows that have missing data for just the columns we are using; do not drop any additional rows . Convert string values to their one-hot encoding. Split the columns as needed into input features and labels.
• Randomly split the dataset into 80% for training and 20% for testing.
• Train a decision tree regressor model to take in year, degree type, and sex to predict the percent of individuals of the specified sex to achieve that degree type in the specified year.
• Use your model to predict on the test set. Calculate the accuracy of your predictions using the mean squared error of the test dataset.

You do not need to anything fancy like find the optimal settings for parameters to maximize performance. We just want you to start simple and train a model from scratch! The reference below has all the methods you will need for this section!

scikit-learn Reference

You can find our reference sheet for machine learning with scikit-learn ScikitLearnReference . This reference sheet has information about general scikit-learn calls that are helpful, as well as how to train the tree models we talked about in class. At the top-right of this page in Ed is a "Fork" button (looks like a fork in the road). This will make your own copy of this Notebook so you can run the code and experiment with anything there! When you open the Workspace, you should see a list of notebooks and CSV files. You can always access this launch page by clikcing the Jupyter logo.

Part 2 Development Strategy

Like in Part 1, it can be difficult to write tests for this section. Machine Learning is all about uncertainty, and it's often difficult to write tests to know what is right. This requires diligence and making sure you are very careful with the method calls you make. To help you with this, we've provided some alternative ways to gain confidence in your result:

• Print your test y values and your predictions to compare them manually. They won't be exactly the same, but you should notice that they have some correlation. For example, I might be concerned if my test y values were [2, 755, …] and my predicted values were [1022, 5...] because they seem to not correlate at all.
• Calculate your mean squared error on your training data as well as your test data. The error should be lower on your training data than on your testing data.

Optional: ML for Time Series

Since this is technically time series data, we should point out that our method for assessing the model's accuracy is slightly wrong (but we will keep it simple for our HW). When working with time series, it is common to use the last rows for your test set rather than random sampling (assuming your data is sorted chronologically). The reason is when working with time series data in machine learning, it's common that our goal is to make a model to help predict the future. By randomly sampling a test set, we are assessing the model on its ability to predict in the past! This is because it might have trained on rows that came after some rows in the test set chronologically. However, this is not a task we particularly care that the model does well at. Instead, by using the last section of the dataset (the most recent in terms of time), we are now assessing its ability to predict into the future from the perspective of its training set.

Even though it's not the best approach to randomly sample here, we ask you to do it anyways. This is because random sampling is the most common method for all other data types.

Part 3: Written Responses

Review the source of the dataset here . For the following reflection questions consider the accuracy of data collected, and how it's used as a public dataset (e.g. presentation of data, publishing in media, etc.). All of your answers should be complete sentences and show thoughtful responses. "No" or "I don't know" or any response like that are not valid responses for any questions. There is not one particularly right answer to these questions, instead, we are looking to see you use your critical thinking and justify your answers!

• Do you think the bar chart from part 1b is an effective data visualization? Explain in 1-2 sentences why or why not.
• Why did you choose the type of plot that you did in part 1c? Explain in a few sentences why you chose this type of plot.
• Datasets can be biased. Bias in data means it might be skewed away from or portray a wrong picture of reality. The data might contain inaccuracies or the methods used to collect the data may have been flawed. Describe a possible bias present in this dataset and why it might have occurred. Your answer should be about 2 or 3 sentences long.

Context : Later in the quarter we will talk about ethics and data science. This question is supposed to be a warm-up to get you thinking about our responsibilities having this power to process data. We are not trying to train to misuse your powers for evil here! Most misuses of data analysis that result in ethical concerns happen unintentionally. As preparation to understand these unintentional consequences, we thought it would be a good exercise to think about a theoretical world where you would willingly try to misuse data.

Congrats! You just got an internship at Evil Corp! Your first task is to come up with an application or analysis that uses this dataset to do something unethical or nefarious. Describe a way that this dataset could be misused in some application or an analysis (potentially using the bias you identified for the last question). Regardless of what nefarious act you choose, evil still has rules: You need to justify why using the data in this is a misuse and why a regular person who is not evil (like you in the real world outside of this problem) would think using the data in this way would be wrong. There are no right answers here of what defines something as unethical, this is why you need to justify your answer! Your response should be 2 to 4 sentences long.

Turn your answers to these question in by writing them in hw3-written.txt and submitting them on Ed

Your submission will be evaluated on the following dimensions:

• Your solution correctly implements the described behaviors. You will have access to some tests when you turn in your assignment, but we will withhold other tests to test your solution when grading. All behavior we test is completely described by the problem specification or shown in an example.
• No method should modify its input parameters.
• Your main method in hw3.py must call every one of the methods you implemented in this assignment. There are no requirements on the format of the output, besides that it should save the files for Part 1 with the proper names specified in Part 1.
• We can run your hw3.py without it crashing or causing any errors or warnings.
• When we run your code, it should produce no errors or warnings.
• All files submitted pass flake8
• All program files should be written with good programming style. This means your code should satisfy the requirements within the CSE 163 Code Quality Guide .
• Any expectations on this page or the sub-pages for the assignment are met as well as all requirements for each of the problems are met.

Make sure you carefully read the bullets above as they may or may not change from assignment to assignment!

A note on allowed material

A lot of students have been asking questions like "Can I use this method or can I use this language feature in this class?". The general answer to this question is it depends on what you want to use, what the problem is asking you to do and if there are any restrictions that problem places on your solution.

There is no automatic deduction for using some advanced feature or using material that we have not covered in class yet, but if it violates the restrictions of the assignment, it is possible you will lose points. It's not possible for us to list out every possible thing you can't use on the assignment, but we can say for sure that you are safe to use anything we have covered in class so far as long as it meets what the specification asks and you are appropriately using it as we showed in class.

For example, some things that are probably okay to use even though we didn't cover them:

• Using the update method on the set class even though I didn't show it in lecture. It was clear we talked about sets and that you are allowed to use them on future assignments and if you found a method on them that does what you need, it's probably fine as long as it isn't violating some explicit restriction on that assignment.
• Using something like a ternary operator in Python. This doesn't make a problem any easier, it's just syntax.

For example, some things that are probably not okay to use:

• Importing some random library that can solve the problem we ask you to solve in one line.
• If the problem says "don't use a loop" to solve it, it would not be appropriate to use some advanced programming concept like recursion to "get around" that restriction.

These are not allowed because they might make the problem trivially easy or violate what the learning objective of the problem is.

You should think about what the spec is asking you to do and as long as you are meeting those requirements, we will award credit. If you are concerned that an advanced feature you want to use falls in that second category above and might cost you points, then you should just not use it! These problems are designed to be solvable with the material we have learned so far so it's entirely not necessary to go look up a bunch of advanced material to solve them.

tl;dr; We will not be answering every question of "Can I use X" or "Will I lose points if I use Y" because the general answer is "You are not forbidden from using anything as long as it meets the spec requirements. If you're unsure if it violates a spec restriction, don't use it and just stick to what we learned before the assignment was released."

This assignment is due by Thursday, July 23 at 23:59 (PDT) .

You should submit your finished hw3.py , and hw3-written.txt on Ed .

You may submit your assignment as many times as you want before the late cutoff (remember submitting after the due date will cost late days). Recall on Ed, you submit by pressing the "Mark" button. You are welcome to develop the assignment on Ed or develop locally and then upload to Ed before marking.

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Top 50 Big Data Interview Questions And Answers – Updated

The era of big data has just begun. With more companies inclined towards big data to run their operations, the demand for talent at an all-time high. What does it mean for you? It only translates into better opportunities if you want to get employed in any of the big data positions. You can choose to become a Data Analyst, Data Scientist, Database administrator, Big Data Engineer, Hadoop Big Data Engineer and so on.  I n this article, we will go through the top 50 big data interview questions related to Big Data.

Also, this article is equally useful for anyone who is preparing for the Hadoop interview as a fresher or experienced as you will also find top Hadoop interview questions in this series.

50 Most Popular Big Data Interview Questions

To give your career an edge, you should be well-prepared for the big data interview questions and answers. Before we start, it is important to understand that the interview is a place where you and the interviewer interact only to understand each other, and not the other way around. Hence, you don’t have to hide anything, just be honest and reply to the questions with honesty. If you feel confused or need more information, feel free to ask questions to the interviewer. Always be honest with your response, and ask questions when required.

Here are top Big Data interview questions and answers with the detailed analysis to the specific questions. For broader questions that’s answer depends on your experience, we will share some tips on how to answer them.

You can also download free eBook/pdf file  in the bottom.

Basic Big Data Interview Questions

Whenever you go for a Big Data interview, the interviewer may ask some basic level questions. Whether you are a fresher or experienced in the big data field, the basic knowledge is required. So, let’s cover some frequently asked basic big data interview questions and answers to crack big data interview.

1. What do you know about the term “Big Data”?

Answer:  Big Data is a term associated with complex and large datasets. A relational database cannot handle big data, and that’s why special tools and methods are used to perform operations on a vast collection of data. Big data enables companies to understand their business better and helps them derive meaningful information from the unstructured and raw data collected on a regular basis. Big data also allows the companies to take better business decisions backed by data.

2. What are the five V’s of Big Data?

Answer: The five V’s of Big data is as follows:

• Volume – Volume represents the volume i.e. amount of data that is growing at a high rate i.e. data volume in Petabytes
• Velocity – Velocity is the rate at which data grows. Social media contributes a major role in the velocity of growing data.
• Variety – Variety refers to the different data types i.e. various data formats like text, audios, videos, etc.
• Veracity – Veracity refers to the uncertainty of available data. Veracity arises due to the high volume of data that brings incompleteness and inconsistency.
• Value – Value refers to turning data into value. By turning accessed big data into values, businesses may generate revenue.

Note:  This is one of the basic and significant questions asked in the big data interview. You can choose to explain the five V’s in detail if you see the interviewer is interested to know more. However, the names can even be mentioned if you are asked about the term “Big Data”.

3. Tell us how big data and Hadoop are related to each other.

Answer:  Big data and Hadoop are almost synonyms terms. With the rise of big data, Hadoop, a framework that specializes in big data operations also became popular. The framework can be used by professionals to analyze big data and help businesses to make decisions.

Note:  This question is commonly asked in a big data interview.  Y ou can go further to answer this question and try to explain the main components of Hadoop.

4. How is big data analysis helpful in increasing business revenue?

Answer: Big data analysis has become very important for the businesses. It helps businesses to differentiate themselves from others and increase the revenue. Through predictive analytics, big data analytics provides businesses customized recommendations and suggestions. Also, big data analytics enables businesses to launch new products depending on customer needs and preferences. These factors make businesses earn more revenue, and thus companies are using big data analytics. Companies may encounter a significant increase of 5-20% in revenue by implementing big data analytics. Some popular companies those are using big data analytics to increase their revenue is – Walmart, LinkedIn, Facebook, Twitter, Bank of America etc.

5. Explain the steps to be followed to deploy a Big Data solution.

Answer: Followings are the three steps that are followed to deploy a Big Data Solution –

i. Data Ingestion

The first step for deploying a big data solution is the data ingestion i.e. extraction of data from various sources. The data source may be a CRM like Salesforce, Enterprise Resource Planning System like SAP, RDBMS like MySQL or any other log files, documents, social media feeds etc. The data can be ingested either through batch jobs or real-time streaming. The extracted data is then stored in HDFS.

ii. Data Storage

After data ingestion, the next step is to store the extracted data. The data either be stored in HDFS or NoSQL database (i.e. HBase). The HDFS storage works well for sequential access whereas HBase for random read/write access.

iii. Data Processing

The final step in deploying a big data solution is the data processing. The data is processed through one of the processing frameworks like Spark, MapReduce, Pig, etc.

Also Read: Top HBase Interview Questions with Detailed Answers

6. Define respective components of HDFS and YARN

Answer: The two main components of HDFS are-

• NameNode – This is the master node for processing metadata information for data blocks within the HDFS
• DataNode/Slave node – This is the node which acts as slave node to store the data, for processing and use by the NameNode

In addition to serving the client requests, the NameNode executes either of two following roles –

• CheckpointNode – It runs on a different host from the NameNode
• BackupNode- It is a read-only NameNode which contains file system metadata information excluding the block locations

The two main components of YARN are –

• ResourceManager– This component receives processing requests and accordingly allocates to respective NodeManagers depending on processing needs.
• NodeManager– It executes tasks on each single Data Node

Preparing for HDFS interview? Here we cover the most common HDFS interview questions and answers to help you crack the interview!

7. Why is Hadoop used for Big Data Analytics?

Answer:  Since data analysis has become one of the key parameters of business, hence, enterprises are dealing with massive amount of structured, unstructured and semi-structured data. Analyzing unstructured data is quite difficult where Hadoop takes major part with its capabilities of  

• Data collection

Moreover, Hadoop is open source and runs on commodity hardware. Hence it is a cost-benefit solution for businesses.

8. What is fsck?

Answer:  fsck stands for File System Check . It is a command used by HDFS. This command is used to check inconsistencies and if there is any problem in the file. For example, if there are any missing blocks for a file, HDFS gets notified through this command.

9. What are the main differences between NAS (Network-attached storage) and HDFS?

Answer: The main differences between NAS (Network-attached storage) and HDFS –

• HDFS runs on a cluster of machines while NAS runs on an individual machine. Hence, data redundancy is a common issue in HDFS. On the contrary, the replication protocol is different in case of NAS. Thus the chances of data redundancy are much less.
• Data is stored as data blocks in local drives in case of HDFS. In case of NAS, it is stored in dedicated hardware.

10. What is the Command to format the NameNode?

Answer:   $ hdfs namenode -format

Big data is not just what you think, it’s a broad spectrum. There are a number of career options in Big Data World. Here is an interesting and explanatory visual on Big Data Careers .

Experience-based Big Data Interview Questions

If you have some considerable experience of working in Big Data world, you will be asked a number of questions in your big data interview based on your previous experience. These questions may be simply related to your experience or scenario based. So, get prepared with these best Big data interview questions and answers –

11. Do you have any Big Data experience? If so, please share it with us.

How to Approach:  There is no specific answer to the question as it is a subjective question and the answer depends on your previous experience. Asking this question during a big data interview, the interviewer wants to understand your previous experience and is also trying to evaluate if you are fit for the project requirement.

So, how will you approach the question? If you have previous experience, start with your duties in your past position and slowly add details to the conversation. Tell them about your contributions that made the project successful. This question is generally, the 2 nd  or 3 rd  question asked in an interview. The later questions are based on this question, so answer it carefully. You should also take care not to go overboard with a single aspect of your previous job. Keep it simple and to the point.

12. Do you prefer good data or good models? Why?

How to Approach:  This is a tricky question but generally asked in the big data interview. It asks you to choose between good data or good models. As a candidate, you should try to answer it from your experience. Many companies want to follow a strict process of evaluating data, means they have already selected data models. In this case, having good data can be game-changing. The other way around also works as a model is chosen based on good data.

As we already mentioned, answer it from your experience. However, don’t say that having both good data and good models is important as it is hard to have both in real life projects.

13. Will you optimize algorithms or code to make them run faster?

How to Approach:  The answer to this question should always be “Yes.” Real world performance matters and it doesn’t depend on the data or model you are using in your project.

The interviewer might also be interested to know if you have had any previous experience in code or algorithm optimization. For a beginner, it obviously depends on which projects he worked on in the past. Experienced candidates can share their experience accordingly as well. However, be honest about your work, and it is fine if you haven’t optimized code in the past. Just let the interviewer know your real experience and you will be able to crack the big data interview.

14. How do you approach data preparation?

How to Approach:  Data preparation is one of the crucial steps in big data projects. A big data interview may involve at least one question based on data preparation. When the interviewer asks you this question, he wants to know what steps or precautions you take during data preparation.

As you already know, data preparation is required to get necessary data which can then further be used for modeling purposes. You should convey this message to the interviewer. You should also emphasize the type of model you are going to use and reasons behind choosing that particular model. Last, but not the least, you should also discuss important data preparation terms such as transforming variables, outlier values, unstructured data, identifying gaps, and others.

15. How would you transform unstructured data into structured data?

How to Approach:  Unstructured data is very common in big data. The unstructured data should be transformed into structured data to ensure proper data analysis. You can start answering the question by briefly differentiating between the two. Once done, you can now discuss the methods you use to transform one form to another. You might also share the real-world situation where you did it. If you have recently been graduated, then you can share information related to your academic projects.

By answering this question correctly, you are signaling that you understand the types of data, both structured and unstructured, and also have the practical experience to work with these. If you give an answer to this question specifically, you will definitely be able to crack the big data interview.

16. Which hardware configuration is most beneficial for Hadoop jobs?

Dual processors or core machines with a configuration of  4 / 8 GB RAM and ECC memory is ideal for running Hadoop operations. However, the hardware configuration varies based on the project-specific workflow and process flow and need customization accordingly.

17. What happens when two users try to access the same file in the HDFS?

HDFS NameNode supports exclusive write only. Hence, only the first user will receive the grant for file access and the second user will be rejected.

18. How to recover a NameNode when it is down?

The following steps need to execute to make the Hadoop cluster up and running:

• Use the FsImage which is file system metadata replica to start a new NameNode. 
• Configure the DataNodes and also the clients to make them acknowledge the newly started NameNode.
• Once the new NameNode completes loading the last checkpoint FsImage which has received enough block reports from the DataNodes, it will start to serve the client. 

In case of large Hadoop clusters, the NameNode recovery process consumes a lot of time which turns out to be a more significant challenge in case of routine maintenance.

19. What do you understand by Rack Awareness in Hadoop?

It is an algorithm applied to the NameNode to decide how blocks and its replicas are placed. Depending on rack definitions network traffic is minimized between DataNodes within the same rack. For example, if we consider replication factor as 3, two copies will be placed on one rack whereas the third copy in a separate rack.

20. What is the difference between “HDFS Block” and “Input Split”?

The HDFS divides the input data physically into blocks for processing which is known as HDFS Block.

Input Split is a logical division of data by mapper for mapping operation.

Enhance your Big Data skills with the experts. Here is the Complete List of Big Data Blogs where you can find latest news, trends, updates, and concepts of Big Data.

Basic Big Data Hadoop Interview Questions

Hadoop is one of the most popular Big Data frameworks, and if you are going for a Hadoop interview prepare yourself with these basic level interview questions for Big Data Hadoop. These questions will be helpful for you whether you are going for a Hadoop developer or Hadoop Admin interview.

21. Explain the difference between Hadoop and RDBMS.

Answer: the difference between hadoop and rdbms is as follows – 22. what are the common input formats in hadoop.

Answer: Below are the common input formats in Hadoop –

• Text Input Format – The default input format defined in Hadoop is the Text Input Format.
• Sequence File Input Format – To read files in a sequence, Sequence File Input Format is used.
• Key Value Input Format – The input format used for plain text files (files broken into lines) is the Key Value Input Format.

23. Explain some important features of Hadoop.

Answer: Hadoop supports the storage and processing of big data. It is the best solution for handling big data challenges. Some important features of Hadoop are –

• Open Source – Hadoop is an open source framework which means it is available free of cost. Also, the users are allowed to change the source code as per their requirements.
• Distributed Processing – Hadoop supports distributed processing of data i.e. faster processing. The data in Hadoop HDFS is stored in a distributed manner and MapReduce is responsible for the parallel processing of data.
• Fault Tolerance – Hadoop is highly fault-tolerant. It creates three replicas for each block at different nodes, by default. This number can be changed according to the requirement. So, we can recover the data from another node if one node fails. The detection of node failure and recovery of data is done automatically.
• Reliability – Hadoop stores data on the cluster in a reliable manner that is independent of machine. So, the data stored in Hadoop environment is not affected by the failure of the machine.
• Scalability – Another important feature of Hadoop is the scalability. It is compatible with the other hardware and we can easily ass the new hardware to the nodes.
• High Availability – The data stored in Hadoop is available to access even after the hardware failure. In case of hardware failure, the data can be accessed from another path.

24. Explain the different modes in which Hadoop run.

Answer: Apache Hadoop runs in the following three modes –

• Standalone (Local) Mode – By default, Hadoop runs in a local mode i.e. on a non-distributed, single node. This mode uses the local file system to perform input and output operation. This mode does not support the use of HDFS, so it is used for debugging. No custom configuration is needed for configuration files in this mode.
• Pseudo-Distributed Mode – In the pseudo-distributed mode, Hadoop runs on a single node just like the Standalone mode. In this mode, each daemon runs in a separate Java process. As all the daemons run on a single node, there is the same node for both the Master and Slave nodes.
• Fully – Distributed Mode – In the fully-distributed mode, all the daemons run on separate individual nodes and thus forms a multi-node cluster. There are different nodes for Master and Slave nodes.

25. Explain the core components of Hadoop.

Answer: Hadoop is an open source framework that is meant for storage and processing of big data in a distributed manner. The core components of Hadoop are –

• HDFS (Hadoop Distributed File System) – HDFS is the basic storage system of Hadoop. The large data files running on a cluster of commodity hardware are stored in HDFS. It can store data in a reliable manner even when hardware fails.

• Hadoop MapReduce – MapReduce is the Hadoop layer that is responsible for data processing. It writes an application to process unstructured and structured data stored in HDFS. It is responsible for the parallel processing of high volume of data by dividing data into independent tasks. The processing is done in two phases Map and Reduce. The Map is the first phase of processing that specifies complex logic code and the Reduce is the second phase of processing that specifies light-weight operations.
• YARN – The processing framework in Hadoop is YARN. It is used for resource management and provides multiple data processing engines i.e. data science, real-time streaming, and batch processing.

26. What are the configuration parameters in a “MapReduce” program?

The main configuration parameters in “MapReduce” framework are:

• Input locations of Jobs in the distributed file system
• Output location of Jobs in the distributed file system
• The input format of data
• The output format of data
• The class which contains the map function
• The class which contains the reduce function
• JAR file which contains the mapper, reducer and the driver classes

27. What is a block in HDFS and what is its default size in Hadoop 1 and Hadoop 2? Can we change the block size?

Blocks are smallest continuous data storage in a hard drive. For HDFS, blocks are stored across Hadoop cluster.

• The default block size in Hadoop 1 is: 64 MB
• The default block size in Hadoop 2 is: 128 MB

Yes, we can change block size by using the parameter – dfs.block.size located in the hdfs-site.xml file.

28. What is  Distributed Cache in a MapReduce Framework

Distributed Cache is a feature of Hadoop MapReduce framework to cache files for applications. Hadoop framework makes cached files available for every map/reduce tasks running on the data nodes. Hence, the data files can access the cache file as a local file in the designated job.

29. What are the three running modes of Hadoop?

The three running modes of Hadoop are as follows:

i. Standalone or local : This is the default mode and does not need any configuration. In this mode, all the following components of Hadoop uses local file system and runs on a single JVM –

• ResourceManager
• NodeManager

ii. Pseudo-distributed : In this mode, all the master and slave Hadoop services are deployed and executed on a single node.

iii. Fully distributed : In this mode, Hadoop master and slave services are deployed and executed on separate nodes.

30. Explain JobTracker in Hadoop

JobTracker is a JVM process in Hadoop to submit and track MapReduce jobs.

JobTracker performs the following activities in Hadoop in a sequence –

• JobTracker receives jobs that a client application submits to the job tracker
• JobTracker notifies NameNode to determine data node
• JobTracker allocates TaskTracker nodes based on available slots.
• it submits the work on allocated TaskTracker Nodes,
• JobTracker monitors the TaskTracker nodes.
• When a task fails, JobTracker is notified and decides how to reallocate the task.

Prepare yourself for the next Hadoop Job Interview with Top 50 Hadoop Interview Questions and Answers.

Hadoop Developer Interview Questions for Fresher

It is not easy to crack Hadoop developer interview but the preparation can do everything. If you are a fresher, learn the Hadoop concepts and prepare properly. Have a good knowledge of the different file systems, Hadoop versions, commands, system security, etc.  Here are few questions that will help you pass the Hadoop developer interview.

31. What are the different configuration files in Hadoop?

Answer: The different configuration files in Hadoop are –

core-site.xml – This configuration file contains Hadoop core configuration settings, for example, I/O settings, very common for MapReduce and HDFS. It uses hostname a port.

mapred-site.xml – This configuration file specifies a framework name for MapReduce by setting mapreduce.framework.name

hdfs-site.xml – This configuration file contains HDFS daemons configuration settings. It also specifies default block permission and replication checking on HDFS.

yarn-site.xml – This configuration file specifies configuration settings for ResourceManager and NodeManager.

32. What are the differences between Hadoop 2 and Hadoop 3?

Answer: Following are the differences between Hadoop 2 and Hadoop 3 –

33. How can you achieve security in Hadoop?

Answer: Kerberos are used to achieve security in Hadoop. There are 3 steps to access a service while using Kerberos, at a high level. Each step involves a message exchange with a server.

• Authentication – The first step involves authentication of the client to the authentication server, and then provides a time-stamped TGT (Ticket-Granting Ticket) to the client.
• Authorization – In this step, the client uses received TGT to request a service ticket from the TGS (Ticket Granting Server).
• Service Request – It is the final step to achieve security in Hadoop. Then the client uses service ticket to authenticate himself to the server.

34. What is commodity hardware?

Answer: Commodity hardware is a low-cost system identified by less-availability and low-quality. The commodity hardware comprises of RAM as it performs a number of services that require RAM for the execution. One doesn’t require high-end hardware configuration or supercomputers to run Hadoop, it can be run on any commodity hardware.

35. How is NFS different from HDFS?

Answer: There are a number of distributed file systems that work in their own way. NFS (Network File System) is one of the oldest and popular distributed file storage systems whereas HDFS (Hadoop Distributed File System) is the recently used and popular one to handle big data. The main differences between NFS and HDFS are as follows –

36. How do Hadoop MapReduce works?

There are two phases of MapReduce operation.

• Map phase – In this phase, the input data is split by map tasks. The map tasks run in parallel. These split data is used for analysis purpose.
• Reduce phase- In this phase, the similar split data is aggregated from the entire collection and shows the result.

37. What is MapReduce? What is the syntax you use to run a MapReduce program?

MapReduce is a programming model in Hadoop for processing large data sets over a cluster of computers, commonly known as HDFS. It is a parallel programming model.

The syntax to run a MapReduce program is – hadoop_jar_file.jar /input_path /output_path .

38. What are the Port Numbers for NameNode, Task Tracker, and Job Tracker?

• NameNode – Port 50070
• Task Tracker – Port 50060
• Job Tracker – Port 50030

39.What are the different file permissions in HDFS for files or directory levels?

Hadoop distributed file system (HDFS) uses a specific permissions model for files and directories. Following user levels are used in HDFS –

For each of the user mentioned above following permissions are applicable –

• execute(x).

Above mentioned permissions work differently for files and directories.

For files –

• The r permission is for reading a file
• The w permission is for writing a file.

For directories –

• The r permission lists the contents of a specific directory.
• The w permission creates or deletes a directory.
• The X permission is for accessing a child directory.

40.What are the basic parameters of a Mapper?

The basic parameters of a Mapper are

• LongWritable and Text
• Text and IntWritable

Hadoop and Spark are the two most popular big data frameworks. But there is a commonly asked question – do we need Hadoop to run Spark? Watch this video to find the answer to this question.

Hadoop Developer Interview Questions for Experienced

The interviewer has more expectations from an experienced Hadoop developer, and thus his questions are one-level up. So, if you have gained some experience, don’t forget to cover command based, scenario-based, real-experience based questions. Here we bring some sample interview questions for experienced Hadoop developers.

41. How to restart all the daemons in Hadoop?

Answer: To restart all the daemons, it is required to stop all the daemons first. The Hadoop directory contains sbin directory that stores the script files to stop and start daemons in Hadoop.

Use stop daemons command /sbin/stop-all.sh to stop all the daemons and then use /sin/start-all.sh command to start all the daemons again.

42. What is the use of jps command in Hadoop?

Answer: The jps command is used to check if the Hadoop daemons are running properly or not. This command shows all the daemons running on a machine i.e. Datanode, Namenode, NodeManager, ResourceManager etc.

43. Explain the process that overwrites the replication factors in HDFS.

Answer: There are two methods to overwrite the replication factors in HDFS –

Method 1: On File Basis

In this method, the replication factor is changed on the basis of file using Hadoop FS shell. The command used for this is:

$hadoop fs – setrep –w2/my/test_file

Here, test_file is the filename that’s replication factor will be set to 2.

Method 2: On Directory Basis

In this method, the replication factor is changed on directory basis i.e. the replication factor for all the files under a given directory is modified.

$hadoop fs –setrep –w5/my/test_dir

Here, test_dir is the name of the directory, the replication factor for the directory and all the files in it will be set to 5.

44. What will happen with a NameNode that doesn’t have any data?

Answer: A NameNode without any data doesn’t exist in Hadoop. If there is a NameNode, it will contain some data in it or it won’t exist.

45. Explain NameNode recovery process.

Answer: The NameNode recovery process involves the below-mentioned steps to make Hadoop cluster running:

• In the first step in the recovery process, file system metadata replica (FsImage) starts a new NameNode.
• The next step is to configure DataNodes and Clients. These DataNodes and Clients will then acknowledge new NameNode.
• During the final step, the new NameNode starts serving the client on the completion of last checkpoint FsImage loading and receiving block reports from the DataNodes.

Note: Don’t forget to mention, this NameNode recovery process consumes a lot of time on large Hadoop clusters. Thus, it makes routine maintenance difficult. For this reason, HDFS high availability architecture is recommended to use.

46. How Is Hadoop CLASSPATH essential to start or stop Hadoop daemons?

CLASSPATH includes necessary directories that contain jar files to start or stop Hadoop daemons. Hence, setting CLASSPATH is essential to start or stop Hadoop daemons.

However, setting up CLASSPATH every time is not the standard that we follow. Usually CLASSPATH is written inside /etc/hadoop/hadoop-env.sh file. Hence, once we run Hadoop, it will load the CLASSPATH automatically.

47. Why is HDFS only suitable for large data sets and not the correct tool to use for many small files?

This is due to the performance issue of NameNode. Usually, NameNode is allocated with huge space to store metadata for the large-scale file. The metadata is supposed to be a from a single file for optimum space utilization and cost benefit. In case of small size files, NameNode does not utilize the entire space which is a performance optimization issue.

48. Why do we need Data Locality in Hadoop? Explain.

Datasets in HDFS store as blocks in DataNodes the Hadoop cluster. During the execution of a MapReduce job the individual Mapper processes the blocks (Input Splits). If the data does not reside in the same node where the Mapper is executing the job, the data needs to be copied from the DataNode over the network to the mapper DataNode.

Now if a MapReduce job has more than 100 Mapper and each Mapper tries to copy the data from other DataNode in the cluster simultaneously, it would cause serious network congestion which is a big performance issue of the overall system. Hence, data proximity to the computation is an effective and cost-effective solution which is technically termed as Data locality in Hadoop. It helps to increase the overall throughput of the system.

Data locality can be of three types :

• Data local – In this type data and the mapper resides on the same node. This is the closest proximity of data and the most preferred scenario.
• Rack Local – In this scenarios mapper and data reside on the same rack but on the different data nodes.
• Different Rack – In this scenario mapper and data reside on the different racks.

49. DFS can handle a large volume of data then why do we need Hadoop framework?

Hadoop is not only for storing large data but also to process those big data. Though DFS(Distributed File System) too can store the data, but it lacks below features-

• It is not fault tolerant
• Data movement over a network depends on bandwidth.

50. What is Sequencefileinputformat?

Hadoop uses a specific file format which is known as Sequence file. The sequence file stores data in a serialized key-value pair. Sequencefileinputformat is an input format to read sequence files.

Final Words

Big Data world is expanding continuously and thus a number of opportunities are arising for the Big Data professionals. This top Big Data interview Q & A set will surely help you in your interview. However, we can’t neglect the importance of certifications. So, if you want to demonstrate your skills to your interviewer during big data interview get certified and add a credential to your resume.

If you have any question regarding Big Data, just leave a comment below. Our Big Data experts will be happy to help you.

Good Luck with your interview!

Expecting to prepare offline with these Big Data interview questions and answers? Download  Big Data FREE EBOOK  Here!

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29 thoughts on “top 50 big data interview questions and answers – updated”.

Good Contents !!

Thank you Rohit.

Your post is helpful to me to prepare for hadoop interview. Thank you for your post.

For Hadoop Interview, we have covered top 50 Hadoop interview questions with detailed answers: https://www.whizlabs.com/blog/top-50-hadoop-interview-questions/

This site is absolutely fabulous!

Thanks for this valuable information.

Interviews always create some tensed situation and to make you feel easy about them you have provided some nice and important programming interview questions which will be very useful for people who are preparing for interviews. Going to save this for sure.

Nice article. It consists of technical question and answers for Big data Interview.

Hello, Thank you for this interview questions ..This will be very helpful..You cover each and every thing very clearly..Please provide interview question for AWS..I used to follow you blog since long time.looking forward for some more blogs from you..Thank you once again

Here is the Interview Questions for AWS:

https://www.whizlabs.com/blog/aws-database-interview-questions/ https://www.whizlabs.com/blog/aws-cloud-support-engineer-interview-questions/ https://www.whizlabs.com/blog/aws-developer-interview-questions/ https://www.whizlabs.com/blog/aws-vpc-interview-questions/ https://www.whizlabs.com/blog/aws-solution-architect-interview-questions/

Merci , le contenu est riche

Thank you Ghizlane

Hi ,This blog is teally very helpful…i need your suggestion. I have total 6.2 years of it experience as DBA . I want to switch company in big data developer how can I tell them real project experience…

You have only one option for this. You can meet any of your friends working on big data technologies and know about their project.

Q1. Can we change the block size in Hadoop after i have spun my clusters? Q2. If yes how could we achieve this and how much effort is required ? Q3. How can we decommission and commission a data node(answer with commands will really help)?

great job sir,it is very helpful for me

very informative content to get into the Bigdata

How about connections being made to Big Data? Some Data Manipulation questions etc? How can we connect to Big Data from (assuming) C#, Java etc?

Awesome information. Thanks for such a great content.

I really recommend this article for big data informatics

How is big data affecting the current software section of programming?

Thanks for your information .

Great read! Thank you for such useful insights. Visit here for latest tech courses on Talend Big Data training

Thanks for sharing such a great Information! Waiting for more updates like this.

enjoy, lead to I found exactly what I used to be taking a look for.

Nice blog. Thanks for sharing your experiences with us and keep going on See more https://www.gologica.com/elearning/why-do-we-need-big-data-hadoop/

I think other web-site proprietors should take this website as an model, very clean and excellent user genial style and design, let alone the content. You are an expert in this topic!

No company can operate without data in modern times. With large volumes of data being created each second from transactions, customer logs, sales figures, and company stakeholders, data is the key fuel that steers a company forward. All these inbound data collects into piles and forms a huge set of data known as Big Data.

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What is Big Data Analytics ? – Definition, Working, Benefits

Big data analysis uses advanced analytical methods that can extract important business insights from bulk datasets. Within these datasets lies both structured (organized) and unstructured (unorganized) data. Its applications cover different industries such as healthcare, education, insurance, AI, retail, and manufacturing. By analyzing this data, organizations get better insight on what is good and what is bad, so they can make the necessary improvements, develop the production system, and increase profitability.

What is Big- Data Analytics?

This guide will discuss in greater detail the concept of big data analytics and how it impacts the decision making process in many parts of the corporate world. You will also know the different types of analyses that are used in big data, the list of the commonly used tools and the courses that can be recommended for you to start your journey towards the data analytics career

Table of Content

What is Big-Data Analytics?

How does big data analytics work, types of big data analytics, big data analytics technologies and tools, benefits of big data analytics, challenges of big data analytics, usage of big data analytics, faqs on big data analytics.

Big data analytics is all about crunching massive amounts of information to uncover hidden trends, patterns, and relationships. It’s like sifting through a giant mountain of data to find the gold nuggets of insight.

Here’s a breakdown of what it involves:

• Collecting Data: Such data is coming from various sources such as social media, web traffic, sensors and customer reviews.
• Cleaning the Data: Imagine having to assess a pile of rocks that included some gold pieces in it. You would have to clean the dirt and the debris first. When data is being cleaned, mistakes must be fixed, duplicates must be removed and the data must be formatted properly.
• Analyzing the Data: It is here that the wizardry takes place. Data analysts employ powerful tools and techniques to discover patterns and trends. It is the same thing as looking for a specific pattern in all those rocks that you sorted through.

The multi-industrial utilization of big data analytics spans from healthcare to finance to retail. Through their data, companies can make better decisions, become more efficient, and get a competitive advantage.

Big Data Analytics is a powerful tool which helps to find the potential of large and complex datasets. To get better understanding, let’s break it down into key steps:

• Data Collection: Data is the core of Big Data Analytics. It is the gathering of data from different sources such as the customers’ comments, surveys, sensors, social media, and so on. The primary aim of data collection is to compile as much accurate data as possible. The more data, the more insights.
• Data Cleaning (Data Preprocessing): The next step is to process this information. It often requires some cleaning. This entails the replacement of missing data, the correction of inaccuracies, and the removal of duplicates. It is like sifting through a treasure trove, separating the rocks and debris and leaving only the valuable gems behind.
• Data Processing: After that we will be working on the data processing. This process contains such important stages as writing, structuring, and formatting of data in a way it will be usable for the analysis. It is like a chef who is gathering the ingredients before cooking. Data processing turns the data into a format suited for analytics tools to process.
• Data Analysis: Data analysis is being done by means of statistical, mathematical, and machine learning methods to get out the most important findings from the processed data. For example, it can uncover customer preferences, market trends, or patterns in healthcare data.
• Data Visualization: Data analysis usually is presented in visual form, for illustration – charts, graphs and interactive dashboards. The visualizations provided a way to simplify the large amounts of data and allowed for decision makers to quickly detect patterns and trends.
• Data Storage and Management: The stored and managed analyzed data is of utmost importance. It is like digital scrapbooking. May be you would want to go back to those lessons in the long run, therefore, how you store them has great importance. Moreover, data protection and adherence to regulations are the key issues to be addressed during this crucial stage.
• Continuous Learning and Improvement: Big data analytics is a continuous process of collecting, cleaning, and analyzing data to uncover hidden insights. It helps businesses make better decisions and gain a competitive edge.

Big Data Analytics comes in many different types, each serving a different purpose:

• Descriptive Analytic s: This type helps us understand past events. In social media, it shows performance metrics, like the number of likes on a post.
• Diagnostic Analytics : In Diagnostic analytics delves deeper to uncover the reasons behind past events. In healthcare, it identifies the causes of high patient re-admissions.
• Predictive Analytics: Predictive analytics forecasts future events based on past data. Weather forecasting, for example, predicts tomorrow’s weather by analyzing historical patterns.
• Prescriptive Analytics: However, this category not only predicts results but also offers recommendations for action to achieve the best results. In e-commerce, it may suggest the best price for a product to achieve the highest possible profit.
• Real-time Analytics: The key function of real-time analytics is data processing in real time. It swiftly allows traders to make decisions based on real-time market events.
• Spatial Analytics: Spatial analytics is about the location data. In urban management, it optimizes traffic flow from the data unde the sensors and cameras to minimize the traffic jam.
• Text Analytics: Text analytics delves into the unstructured data of text. In the hotel business, it can use the guest reviews to enhance services and guest satisfaction.

These types of analytics serve different purposes, making data understandable and actionable. Whether it’s for business, healthcare, or everyday life, Big Data Analytics provides a range of tools to turn data into valuable insights, supporting better decision-making.

Big Data Analytics relies on various technologies and tools that might sound complex, let’s simplify them:

• Hadoop: Imagine Hadoop as an enormous digital warehouse. It’s used by companies like Amazon to store tons of data efficiently. For instance, when Amazon suggests products you might like, it’s because Hadoop helps manage your shopping history.
• Spark: Think of Spark as the super-fast data chef. Netflix uses it to quickly analyze what you watch and recommend your next binge-worthy show.
• NoSQL Databases: NoSQL databases, like MongoDB , are like digital filing cabinets that Airbnb uses to store your booking details and user data. These databases are famous because of their quick and flexible, so the platform can provide you with the right information when you need it.
• Tableau: Tableau is like an artist that turns data into beautiful pictures. The World Bank uses it to create interactive charts and graphs that help people understand complex economic data.
• Python and R: Python and R are like magic tools for data scientists. They use these languages to solve tricky problems. For example, Kaggle uses them to predict things like house prices based on past data.
• Machine Learning Frameworks (e.g., TensorFlow): In Machine learning frameworks are the tools who make predictions. Airbnb uses TensorFlow to predict which properties are most likely to be booked in certain areas. It helps hosts make smart decisions about pricing and availability.

These tools and technologies are the building blocks of Big Data Analytics and helps organizations gather, process, understand, and visualize data, making it easier for them to make decisions based on information.

Big Data Analytics offers a host of real-world advantages, and let’s understand with examples:

• Informed Decisions: Imagine a store like Walmart. Big Data Analytics helps them make smart choices about what products to stock. This not only reduces waste but also keeps customers happy and profits high.
• Enhanced Customer Experiences: Think about Amazon. Big Data Analytics is what makes those product suggestions so accurate. It’s like having a personal shopper who knows your taste and helps you find what you want.
• Fraud Detection: Credit card companies, like MasterCard, use Big Data Analytics to catch and stop fraudulent transactions. It’s like having a guardian that watches over your money and keeps it safe.
• Optimized Logistics: FedEx, for example, uses Big Data Analytics to deliver your packages faster and with less impact on the environment. It’s like taking the fastest route to your destination while also being kind to the planet.

While Big Data Analytics offers incredible benefits, it also comes with its set of challenges:

• Data Overload: Consider Twitter, where approximately 6,000 tweets are posted every second. The challenge is sifting through this avalanche of data to find valuable insights.
• Data Quality: If the input data is inaccurate or incomplete, the insights generated by Big Data Analytics can be flawed. For example, incorrect sensor readings could lead to wrong conclusions in weather forecasting.
• Privacy Concerns: With the vast amount of personal data used, like in Facebook’s ad targeting, there’s a fine line between providing personalized experiences and infringing on privacy.
• Security Risks: With cyber threats increasing, safeguarding sensitive data becomes crucial. For instance, banks use Big Data Analytics to detect fraudulent activities, but they must also protect this information from breaches.
• Costs: Implementing and maintaining Big Data Analytics systems can be expensive. Airlines like Delta use analytics to optimize flight schedules, but they need to ensure that the benefits outweigh the costs.

Overcoming these challenges is essential to fully harness the power of Big Data Analytics. Businesses and organizations must tread carefully, ensuring they make the most of the insights while addressing these obstacles effectively.

Big Data Analytics has a significant impact in various sectors:

• Healthcare: It aids in precise diagnoses and disease prediction, elevating patient care.
• Retail: Amazon’s use of Big Data Analytics offers personalized product recommendations based on your shopping history, creating a more tailored and enjoyable shopping experience.
• Finance: Credit card companies such as Visa rely on Big Data Analytics to swiftly identify and prevent fraudulent transactions, ensuring the safety of your financial assets.
• Transportation: Companies like Uber use Big Data Analytics to optimize drivers’ routes and predict demand, reducing wait times and improving overall transportation experiences.
• Agriculture: Farmers make informed decisions, boosting crop yields while conserving resources.
• Manufacturing: Companies like General Electric (GE) use Big Data Analytics to predict machinery maintenance needs, reducing downtime and enhancing operational efficiency.

Big Data Analytics is a game-changer that’s shaping a smarter future. From improving healthcare and personalizing shopping to securing finances and predicting demand, it’s transforming various aspects of our lives. However, Challenges like managing overwhelming data and safeguarding privacy are real concerns. In our world flooded with data, Big Data Analytics acts as a guiding light. It helps us make smarter choices, offers personalized experiences, and uncovers valuable insights. It’s a powerful and stable tool that promises a better and more efficient future for everyone.

Q1. What industries benefit the most from Big Data Analytics?

Big Data Analytics finds applications in various industries, but it has a significant impact on healthcare, retail, finance, transportation, and agriculture, among others.

Q2. How does data cleaning work in Big Data Analytics?

Data cleaning involves identifying and rectifying errors and inconsistencies in raw data to ensure its accuracy before analysis.

Q3. Can small businesses benefit from Big Data Analytics, or is it only for large corporations?

Small businesses can also benefit from Big Data Analytics. There are tools and solutions designed to suit smaller operations, helping them make data-driven decisions and improve their services.

Q4. Is Big Data Analytics only about analyzing data, or does it also involve data storage?

Big Data Analytics primarily focuses on data analysis. Data storage is typically handled by separate technologies like data warehouses and cloud storage solutions.

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Big Data Analytics Assignment

Task: Worldwide Influence of Big Data Analytics on the Business Priorities and Decision-making Big Data analytics has entirely transformed the approaches as well as modes of the recent business scenarios and this particular concept is simply comprised of four important attributes such as value, velocity, volume as well as variety (Chen, Chiang and Storey 2012). This particular area of research can also result in the useful insights that in turn would aid the better strategic decisions in relation to the businesses. The concept of the big data analytics has risen beyond the storage of numerous information and it has also made the use of the analytical methods iterative along with the ongoing trends of marketplaces in the world of the mobile applications (LaValle er al., 2011). For an instance, businesses in today’s world are capable of analyzing the information on an immediate basis along with the speed of in-memory and Hadoop analytics combined with the capability of analyzing the new data sources (Demirkan and Delen 2013). Therefore, at today’s date, the organizations all over the globe are significantly utilizing the Big Data in driving the decisions of businesses as well as enhancing and improvising the ROI and the performances of the businesses (Chen, Chiang and Storey 2012). Big Data Analytics is a widely accepted or considered topic in the course and the profession of the engineering management as it has been turned into an impressive innovation in the engineering ground because it deals with offering numerous new ways of several technology integration.

Important Research Question How does Big Data Analytics influence the business decision-making and business priorities as well? Independent Variable - Big Data Analytics Dependent Variables - Decision-making and Business Priorities

Clarity on the Question Big Data Analytics deals with helping the companies in harnessing their data as well as utilizing it for identifying the new scopes, which in turn can result in the smarter moves of businesses, happier consumers, higher profits as well as the more efficient business operations (LaValle er al., 2011). Thus, such capability of working faster and staying agile can five the companies a competitive edge that they did not have before (Chen, Chiang and Storey 2012). Therefore, in order to make successful the application of the Big Data Analytics in the business operations of companies, it is very important to analyze the question mentioned above.

Introduction: Big data analysis is a term that is applied to a set of data that is beyond the preview of traditional database. It is used to store data in bulk thereby making way for management of data in a systematic manner. Organizations operating should go for management of data that would be helpful for future projection and implementation. It is important to note that data storage is a cumbersome procedure so should be stored in a definite chamber. Big data provides the platform wherein storage is made easy and exquisite. In fact with the inclusion of big data storage development in the field of decision making can be seen as it helps in understanding the present scenario from the consolidated stock and helps in delivering in a spontaneous manner. In this data analysis assignment we have strictly followed the format given in marking rubrics to cover in helping the student to cover all the deliverables in the assignment. Using the format given below will help you in drafting the data analysis assignment in a descent way.

Aims and objectives: The aim of this data analysis assignment is to define the possibilities that can be undertaken in the storage of data thereby making way for production in bulk for the welfare of the organization. In fact with the inclusion of this concept initiation can be seen in the development of storage capacity. Understanding the requirements of the consumers is of utmost importance thereby delivering taking into account the possibilities of productive development is always at hands (Gandomi & Haider, 2015).

Objectives: The objectives of this data analysis assignment is to

• Provide the platform wherein bulk quantity of data can be stored thereby making way for introduction of more inputs
• Provide the platform wherein product kept at stock can be provided to usage t any point of time.
• Emphasize on team performance in building a coherent atmosphere in delivering output.
• Employment of personnel in keeping records in delivering and maintaining data in the most comprehensive manner

Research Question: How does Big Data help in decision making for the organization? How convenient it is for the organizational personnel in maintaining data in bulk at a single point of time without any difficulty? How the concept of Big Data would be useful in future possibilities for an organization?

Literature Review: Big data analytics is one of the most developed and advanced means in maintaining forum for management of resources. It would help in building momentum for the organization. It would help in delivering in the most comprehensive manner. It would help in redefining concepts of progressive development. It acts as the forum that would help in understanding tastes and the preferences of the consumer of the consumers thereby act in conformity to it. It is important to note development is possible when there is the amalgamation of top authorities along with the technological experts, administrative experts, quality control experts, administrative experts so and so forth. In fact with the amalgamation of different departments inclusion of productive results can be seen at the outset. Big data is driving the corporate world by storm. It has helps in breaking barriers thereby making way for exclusive performance for the growth and development of the organization at large (García et al., 2016).

Management has improved on a significant manner. It has led to outclass probabilities and thereby turn them into possibilities in the organizational forum. In fact with the inclusion of probabilities development can be seen in the functioning at a rapid pace. Analysing information of the organization has been helpful and convenient thereby making way for progressive development in the performance of the organization. Big Data is the cluster of numerous information that is helpful and conclusive in providing the platform for all round development of the organization in the long run. In fact, with the inclusion of probabilities deliberate attempts have been made by personnel to improve the quality of insight in the functioning of the organization (Assunção et al., 2015).

In the most comprehensive ground, there is scope for all round development once functioning is being undertaken for the betterment of the organization. Onset of probabilities along with possibilities can help in diversifying performance of the organization in the long run. Big data acts as the warehouse that would help in maintaining data that would be used by the organizations in the functioning of the organization in the long run. It is the platform of numerous technology integration. Decision making of the business is being developed and enhancement can be seen in the functioning of the organization .In fact with the adaptation of the decision making future prospects can be developed in the most comprehensive manner. It has comprehensively changed the concept of data storage thereby making way for all round development in the delivaration of performance of the organization. Big Data is one of the conclusive and the most promising aspect in the professional world (Rajaraman, 2016). It has led to conceptualize on the propositions related to the functioning of an organization. In fact with the advent of technology need of the hour has been towards productive development rather than inclined towards promulgation of uncertainties.

In the recent context development is possible through the application of resources that would be helpful in the functioning of an organizational forum. In fact with the inclusion of propositions regarding the functioning of an organization it is important on the part of the officials in deliberately inclined towards development that would help in building a platform that would be helpful in maintaining momentum for future growth of the organization. A definite framework that would help in maintaining, should structure, inclusion of swiftness on the part of the development is necessary in this regime (Hu et al., 2014). Moreover, application of development would help in igniting a forum that would be effective in the development of the organization in the long run.

Data analysis: Data analysis is one of the most important and decisive aspect in the functioning of an management forum. In fact with the inclusion of data analysis one can understand the dimensions of big data thereby can make way for all round development in the undertaking of the same (Hashem et al., 2015). In this context there would be undertaking of qualitative analysis that would help in understanding the pros and cons of the management scenario. Development is possible by the application of resources in the functioning of an organizational forum. In the context of Big Data one might question on the implementation of data and its necessity. Qualitative data has been one of the most important aspects in redefining the propositions of research. It helps in demonstrating the consumer insights thereby making way for understanding the propositions in the long run. Generating consumer insights is the tedious task and most importantly implicit on the part of the developers of management in delivering in the most comprehensive manner (Chen et al., 2014).

Online research helps in understanding the expressions and the opinions of the people that are being asked regarding the particular prospect in this regard. In fact with the application of data analysis it is important to note that development seeks a hike once deliberations are the need of the hour. In fact with the application of development it is necessary to review the propositions in the functioning of management of an organization. It is a challenging proposition on the part of the operators and the expert officials in delivering in the functioning of the management. Deliberate attempt can be made on the part of the execution of the policies of the management thereby making way for all round development for the organization. In fact, taking a note of the records that has been used in the functioning of an organizational forum. Development measures should be undertaken by the management that would help in delivering in the most comprehensive manner. It would lead to deliberations in the functioning of the management as a whole. It is important to note that going for records thereby understanding the scenario is important as would help in improving the quality and prospect of the project (John Walker, 2014).

Big Data is blessing in disguise. It is imperative on the part of the officials in delivering in the most comprehensive manner. In fact there has to be recording of minutes that would help in maintaining records in the functioning of the management as a whole. In order to incite development it is imperative on the part of the management to act in forum thereby making way for productive means that would help in decision making of the organization as a whole. It is important in this context to deliver according to the propositions thereby making way for absolute development in the long run .In order to inculcate this training and development is necessary on the part of the officials in making way for alternatives that would help in maintaining forum for the management of the organization. Collection of data has to be in the integration mode thereby making way for transition time and again. Ineffectiveness has to be eliminated thereby making way for outreach that would be helpful in making propositions for development in the significant manner. In order to incorporate development review management has to be undertaken by the management time and again in order to incorporate development in the long run.

Research can be undertaken by the application of online medium thereby making way for all round development. Analytical tools should be used that would help in transition in the functioning of an organizational forum. In fact with the application of automation in the functioning of an organizational forum it is important to note that development would led to development in the long run. Needs and wants seems to be increasing accordingly. It has led to maintaining big data that would help in meeting the needs of the consumers thereby making way for enhancement in the customer base of the organization. It helps in escalation in yielding positive results for the functioning of the management thereby making inroads for all round development of the organization time and again. Communication plays an important role in the up gradation in the functioning of the organization. In order to incorporate effectiveness in the functioning of the management there has to be incorporation in the functioning of the management making inroads for productive development on a simultaneous basis (Kambatla et al., 2014).

Transition is the need of the hour therefore should be incorporated with the inroads of corporate simulation. Corporate culture should be maintained during the course of proceedings of the management, which is possible in the functioning of the management. In order to incorporate development it is necessary to inculcate transition in the functioning of the management thereby making inroads for effective development in the functioning of the organization in the significant manner. In order to incorporate development it is necessary to go for effective implementation of policies thereby making way for effective results (Talia, 2013).

Gantt chart

Conclusion: From the above evaluation in this data analysis assignment, it can be ascertained that functioning of big data is important to reconcile with the functioning of the management. In order to inculcate this training and development is necessary on the part of the officials in making way for alternatives that would help in maintaining forum for the management of the organization. Collection of data has to be in the integration mode thereby making way for transition time and again. Ineffectiveness has to be eliminated thereby making way for outreach that would be helpful in making propositions for development in the significant manner. In order to incorporate development review management has to be undertaken by the management time and again in order to incorporate development.

Reference Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., & Buyya, R. (2015). Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79, 3-15.

Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), 171-209.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big data preprocessing: methods and prospects. Big Data Analytics, 1(1), 9.

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115.

Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, 652-687.

John Walker, S. (2014). Big data: A revolution that will transform how we live, work, and think.

Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561-2573.

Rajaraman, V. (2016). Big data analytics. Resonance, 21(8), 695-716.

Talia, D. (2013). Clouds for scalable big data analytics. Computer, 46(5), 98-101.

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ASSIGNMENT 1: BIG DATA PROJECT

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Big Data Analytics (CS8091) Notes, Question Papers & Syllabus

APR/MAY 2023 EXAMS

Engineering Graphics (GE3251) [EG] Notes, Question Papers & Syllabus

Basic electrical, electronics and instrumentation engineering (be3252) [beeie] notes, question papers & syllabus, electric circuit analysis (ee3251) [eca] notes, question papers & syllabus.

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• Computer Science and Engineering
• NOC:Big Data Computing (Video) 
• Co-ordinated by : IIT Patna
• Available from : 2018-11-26
• Intro Video
• Introduction to Big Data
• Big Data Enabling Technologies
• Hadoop Stack for Big Data
• Hadoop Distributed File System (HDFS)
• Hadoop MapReduce 1.0
• Hadoop MapReduce 2.0 (Part-I)
• Hadoop MapReduce 2.0 (Part-II)
• MapReduce Examples
• Parallel Programming with Spark
• Introduction to Spark
• Spark Built-in Libraries
• Design of Key-Value Stores
• Data Placement Strategies
• CAP Theorem
• Consistency Solutions
• Design of Zookeeper
• CQL (Cassandra Query Language)
• Design of HBase
• Spark Streaming and Sliding Window Analytics (Part-I)
• Spark Streaming and Sliding Window Analytics (Part-II)
• Sliding Window Analytics
• Introduction to Kafka
• Big Data Machine Learning (Part-I)
• Big Data Machine Learning (Part-II)
• Machine Learning Algorithm K-means using Map Reduce for Big Data Analytics
• Parallel K-means using Map Reduce on Big Data Cluster Analysis
• Decision Trees for Big Data Analytics
• Big Data Predictive Analytics (Part-I)
• Big Data Predictive Analytics (Part-II)
• Parameter Servers
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Data analytics tutorial for beginners: a step-by-step guide, what is data analytics and its future scope in 2024, data analytics with python: use case demo, all the ins and outs of exploratory data analysis, top 5 business intelligence tools, the ultimate guide to qualitative vs. quantitative research.

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Lesson 8 of 12 By Shruti M

Table of Contents

Data analytics is widely used in every sector in the 21st century. A career in the field of data analytics is highly lucrative in today's times, with its career potential increasing by the day. Out of the many job roles in this field, a data analyst's job role is widely popular globally. A data analyst collects and processes data; he/she analyzes large datasets to derive meaningful insights from raw data. 

Your Data Analytics Career is Around The Corner!

General Data Analyst Interview Questions

In an interview, these questions are more likely to appear early in the process and cover data analysis at a high level. 

1. Mention the differences between Data Mining and Data Profiling?

2. define the term 'data wrangling in data analytics..

Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format. Techniques such as merging, grouping, concatenating, joining, and sorting are used to analyze the data. Thereafter it gets ready to be used with another dataset.

3. What are the various steps involved in any analytics project?

This is one of the most basic data analyst interview questions. The various steps involved in any common analytics projects are as follows:

Understanding the Problem

Understand the business problem, define the organizational goals, and plan for a lucrative solution.

Collecting Data

Gather the right data from various sources and other information based on your priorities.

Cleaning Data

Clean the data to remove unwanted, redundant, and missing values, and make it ready for analysis.

Exploring and Analyzing Data

Use data visualization and business intelligence tools , data mining techniques, and predictive modeling to analyze data.

Interpreting the Results

Interpret the results to find out hidden patterns, future trends, and gain insights.

4. What are the common problems that data analysts encounter during analysis?

The common problems steps involved in any analytics project are:

• Handling duplicate 
• Collecting the meaningful right data and the right time
• Handling data purging and storage problems
• Making data secure and dealing with compliance issues

5. Which are the technical tools that you have used for analysis and presentation purposes?

As a data analyst , you are expected to know the tools mentioned below for analysis and presentation purposes. Some of the popular tools you should know are:

MS SQL Server, MySQL

For working with data stored in relational databases

MS Excel, Tableau

For creating reports and dashboards

Python, R, SPSS

For statistical analysis, data modeling, and exploratory analysis

MS PowerPoint

For presentation, displaying the final results and important conclusions 

6. What are the best methods for data cleaning?

• Create a data cleaning plan by understanding where the common errors take place and keep all the communications open.
• Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process .
• Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.
• Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.

7. What is the significance of Exploratory Data Analysis (EDA)?

• Exploratory data analysis (EDA) helps to understand the data better.
• It helps you obtain confidence in your data to a point where you’re ready to engage a machine learning algorithm.
• It allows you to refine your selection of feature variables that will be used later for model building.
• You can discover hidden trends and insights from the data.

8. Explain descriptive, predictive, and prescriptive analytics.

9. what are the different types of sampling techniques used by data analysts.

Sampling is a statistical method to select a subset of data from an entire dataset (population) to estimate the characteristics of the whole population. 

There are majorly five types of sampling methods:

• Simple random sampling
• Systematic sampling
• Cluster sampling
• Stratified sampling
• Judgmental or purposive sampling

10. Describe univariate, bivariate, and multivariate analysis.

Univariate analysis is the simplest and easiest form of data analysis where the data being analyzed contains only one variable. 

Example - Studying the heights of players in the NBA.

Univariate analysis can be described using Central Tendency, Dispersion, Quartiles, Bar charts, Histograms, Pie charts, and Frequency distribution tables.

The bivariate analysis involves the analysis of two variables to find causes, relationships, and correlations between the variables. 

Example – Analyzing the sale of ice creams based on the temperature outside.

The bivariate analysis can be explained using Correlation coefficients, Linear regression, Logistic regression, Scatter plots, and Box plots.

The multivariate analysis involves the analysis of three or more variables to understand the relationship of each variable with the other variables. 

Example – Analysing Revenue based on expenditure.

Multivariate analysis can be performed using Multiple regression, Factor analysis, Classification & regression trees, Cluster analysis, Principal component analysis, Dual-axis charts, etc.

11. What are your strengths and weaknesses as a data analyst?

The answer to this question may vary from a case to case basis. However, some general strengths of a data analyst may include strong analytical skills, attention to detail, proficiency in data manipulation and visualization, and the ability to derive insights from complex datasets. Weaknesses could include limited domain knowledge, lack of experience with certain data analysis tools or techniques, or challenges in effectively communicating technical findings to non-technical stakeholders.

12. What are the ethical considerations of data analysis?

Some of the most the ethical considerations of data analysis includes:

• Privacy: Safeguarding the privacy and confidentiality of individuals' data, ensuring compliance with applicable privacy laws and regulations.
• Informed Consent: Obtaining informed consent from individuals whose data is being analyzed, explaining the purpose and potential implications of the analysis.
• Data Security: Implementing robust security measures to protect data from unauthorized access, breaches, or misuse.
• Data Bias: Being mindful of potential biases in data collection, processing, or interpretation that may lead to unfair or discriminatory outcomes.
• Transparency: Being transparent about the data analysis methodologies, algorithms, and models used, enabling stakeholders to understand and assess the results.
• Data Ownership and Rights: Respecting data ownership rights and intellectual property, using data only within the boundaries of legal permissions or agreements.
• Accountability: Taking responsibility for the consequences of data analysis, ensuring that actions based on the analysis are fair, just, and beneficial to individuals and society.
• Data Quality and Integrity: Ensuring the accuracy, completeness, and reliability of data used in the analysis to avoid misleading or incorrect conclusions.
• Social Impact: Considering the potential social impact of data analysis results, including potential unintended consequences or negative effects on marginalized groups.
• Compliance: Adhering to legal and regulatory requirements related to data analysis, such as data protection laws, industry standards, and ethical guidelines.

13. What are some common data visualization tools you have used?

You should name the tools you have used personally, however here’s a list of the commonly used data visualization tools in the industry:

• Microsoft Power BI
• Google Data Studio
• Matplotlib (Python library)
• Excel (with built-in charting capabilities)
• IBM Cognos Analytics

Data Analyst Interview Questions On Statistics

14. how can you handle missing values in a dataset.

This is one of the most frequently asked data analyst interview questions, and the interviewer expects you to give a detailed answer here, and not just the name of the methods. There are four methods to handle missing values in a dataset.

Listwise Deletion

In the listwise deletion method, an entire record is excluded from analysis if any single value is missing.

Average Imputation 

Take the average value of the other participants' responses and fill in the missing value.

Regression Substitution

You can use multiple-regression analyses to estimate a missing value.

Multiple Imputations

It creates plausible values based on the correlations for the missing data and then averages the simulated datasets by incorporating random errors in your predictions.

15. Explain the term Normal Distribution.

Normal Distribution refers to a continuous probability distribution that is symmetric about the mean. In a graph, normal distribution will appear as a bell curve.

• The mean, median, and mode are equal
• All of them are located in the center of the distribution
• 68% of the data falls within one standard deviation of the mean
• 95% of the data lies between two standard deviations of the mean
• 99.7% of the data lies between three standard deviations of the mean

16. What is Time Series analysis?

Time Series analysis is a statistical procedure that deals with the ordered sequence of values of a variable at equally spaced time intervals. Time series data are collected at adjacent periods. So, there is a correlation between the observations. This feature distinguishes time-series data from cross-sectional data.

Below is an example of time-series data on coronavirus cases and its graph.

17. How is Overfitting different from Underfitting?

This is another frequently asked data analyst interview question, and you are expected to cover all the given differences!

18. How do you treat outliers in a dataset? 

An outlier is a data point that is distant from other similar points. They may be due to variability in the measurement or may indicate experimental errors. 

The graph depicted below shows there are three outliers in the dataset.

To deal with outliers, you can use the following four methods:

• Drop the outlier records
• Cap your outliers data
• Assign a new value
• Try a new transformation

19. What are the different types of Hypothesis testing?

Hypothesis testing is the procedure used by statisticians and scientists to accept or reject statistical hypotheses. There are mainly two types of hypothesis testing:

• Null hypothesis : It states that there is no relation between the predictor and outcome variables in the population. H0 denoted it.  

Example: There is no association between a patient’s BMI and diabetes.

• Alternative hypothesis : It states that there is some relation between the predictor and outcome variables in the population. It is denoted by H1.

Example: There could be an association between a patient’s BMI and diabetes.

20. Explain the Type I and Type II errors in Statistics?

In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.

A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.

21. How would you handle missing data in a dataset?

Ans: The choice of handling technique depends on factors such as the amount and nature of missing data, the underlying analysis, and the assumptions made. It's crucial to exercise caution and carefully consider the implications of the chosen approach to ensure the integrity and reliability of the data analysis. However, a few solutions could be:

• removing the missing observations or variables
• imputation methods including, mean imputation (replacing missing values with the mean of the available data), median imputation (replacing missing values with the median), or regression imputation (predicting missing values based on regression models)
• sensitivity analysis 

22. Explain the concept of outlier detection and how you would identify outliers in a dataset.

Outlier detection is the process of identifying observations or data points that significantly deviate from the expected or normal behavior of a dataset. Outliers can be valuable sources of information or indications of anomalies, errors, or rare events.

It's important to note that outlier detection is not a definitive process, and the identified outliers should be further investigated to determine their validity and potential impact on the analysis or model. Outliers can be due to various reasons, including data entry errors, measurement errors, or genuinely anomalous observations, and each case requires careful consideration and interpretation.

Excel Data Analyst Interview Questions

23. in microsoft excel, a numeric value can be treated as a text value if it precedes with what.

24. What is the difference between COUNT, COUNTA, COUNTBLANK, and COUNTIF in Excel?

• COUNT function returns the count of numeric cells in a range
• COUNTA function counts the non-blank cells in a range
• COUNTBLANK function gives the count of blank cells in a range
• COUNTIF function returns the count of values by checking a given condition

Future-Proof Your AI/ML Career: Top Dos and Don'ts

25. How do you make a dropdown list in MS Excel?

• First, click on the Data tab that is present in the ribbon.
• Under the Data Tools group, select Data Validation.
• Then navigate to Settings > Allow > List.
• Select the source you want to provide as a list array.

26. Can you provide a dynamic range in “Data Source” for a Pivot table?

Yes, you can provide a dynamic range in the “Data Source” of Pivot tables. To do that, you need to create a named range using the offset function and base the pivot table using a named range constructed in the first step.

27. What is the function to find the day of the week for a particular date value?

The get the day of the week, you can use the WEEKDAY() function.

The above function will return 6 as the result, i.e., 17th December is a Saturday.

28. How does the AND() function work in Excel?

AND() is a logical function that checks multiple conditions and returns TRUE or FALSE based on whether the conditions are met.

Syntax: AND(logica1,[logical2],[logical3]....)

In the below example, we are checking if the marks are greater than 45. The result will be true if the mark is >45, else it will be false.

29. Explain how VLOOKUP works in Excel?

VLOOKUP is used when you need to find things in a table or a range by row.

VLOOKUP accepts the following four parameters:

lookup_value - The value to look for in the first column of a table

table - The table from where you can extract value

col_index - The column from which to extract value

range_lookup - [optional] TRUE = approximate match (default). FALSE = exact match

Let’s understand VLOOKUP with an example.

If you wanted to find the department to which Stuart belongs to, you could use the VLOOKUP function as shown below:

Here, A11 cell has the lookup value, A2:E7 is the table array, 3 is the column index number with information about departments, and 0 is the range lookup. 

If you hit enter, it will return “Marketing”, indicating that Stuart is from the marketing department.

30. What function would you use to get the current date and time in Excel?

In Excel, you can use the TODAY() and NOW() function to get the current date and time.

31. Using the below sales table, calculate the total quantity sold by sales representatives whose name starts with A, and the cost of each item they have sold is greater than 10.

You can use the SUMIFS() function to find the total quantity.

For the Sales Rep column, you need to give the criteria as “A*” - meaning the name should start with the letter “A”. For the Cost each column, the criteria should be “>10” - meaning the cost of each item is greater than 10.

The result is 13 .

33. Using the data given below, create a pivot table to find the total sales made by each sales representative for each item. Display the sales as % of the grand total.

• Select the entire table range, click on the Insert tab and choose PivotTable

• Select the table range and the worksheet where you want to place the pivot table

• Drag Sale total on to Values, and Sales Rep and Item on to Row Labels. It will give the sum of sales made by each representative for every item they have sold.

• Right-click on “Sum of Sale Total’ and expand Show Values As to select % of Grand Total.

• Below is the resultant pivot table.

SQL Interview Questions for Data Analysts

34. how do you subset or filter data in sql.

To subset or filter data in SQL, we use WHERE and HAVING clauses.

Consider the following movie table.

Using this table, let’s find the records for movies that were directed by Brad Bird.

Now, let’s filter the table for directors whose movies have an average duration greater than 115 minutes.

35. What is the difference between a WHERE clause and a HAVING clause in SQL?

Answer all of the given differences when this data analyst interview question is asked, and also give out the syntax for each to prove your thorough knowledge to the interviewer.

Syntax of WHERE clause:

SELECT column1, column2, ... FROM table_name WHERE condition;

Syntax of HAVING clause;

SELECT column_name(s) FROM table_name WHERE condition GROUP BY column_name(s) HAVING condition ORDER BY column_name(s);

36. Is the below SQL query correct? If not, how will you rectify it?

The query stated above is incorrect as we cannot use the alias name while filtering data using the WHERE clause. It will throw an error.

37. How are Union, Intersect, and Except used in SQL?

The Union operator combines the output of two or more SELECT statements.

SELECT column_name(s) FROM table1 UNION SELECT column_name(s) FROM table2;

Let’s consider the following example, where there are two tables - Region 1 and Region 2.

To get the unique records, we use Union.

The Intersect operator returns the common records that are the results of 2 or more SELECT statements.

SELECT column_name(s) FROM table1 INTERSECT SELECT column_name(s) FROM table2;

The Except operator returns the uncommon records that are the results of 2 or more SELECT statements.

SELECT column_name(s) FROM table1 EXCEPT SELECT column_name(s) FROM table2;

Below is the SQL query to return uncommon records from region 1.

38. What is a Subquery in SQL?

A Subquery in SQL is a query within another query. It is also known as a nested query or an inner query. Subqueries are used to enhance the data to be queried by the main query. 

It is of two types - Correlated and Non-Correlated Query.

Below is an example of a subquery that returns the name, email id, and phone number of an employee from Texas city.

SELECT name, email, phone

FROM employee

WHERE emp_id IN (

SELECT emp_id

WHERE city = 'Texas');

39. Using the product_price table, write an SQL query to find the record with the fourth-highest market price.

Fig: Product Price table

select top 4 * from product_price order by mkt_price desc;

Now, select the top one from the above result that is in ascending order of mkt_price.

40. From the product_price table, write an SQL query to find the total and average market price for each currency where the average market price is greater than 100, and the currency is in INR or AUD.

The SQL query is as follows:

The output of the query is as follows:

41. Using the product and sales order detail table, find the products with total units sold greater than 1.5 million.

Fig: Products table

Fig: Sales order detail table

We can use an inner join to get records from both the tables. We’ll join the tables based on a common key column, i.e., ProductID.

The result of the SQL query is shown below.

42. How do you write a stored procedure in SQL ?

You must be prepared for this question thoroughly before your next data analyst interview. The stored procedure is an SQL script that is used to run a task several times.

Let’s look at an example to create a stored procedure to find the sum of the first N natural numbers' squares.

• Create a procedure by giving a name, here it’s squaresum1
• Declare the variables
• Write the formula using the set statement
• Print the values of the computed variable
• To run the stored procedure, use the EXEC command

Output: Display the sum of the square for the first four natural numbers

43. Write an SQL stored procedure to find the total even number between two users given numbers.

Here is the output to print all even numbers between 30 and 45.

Tableau Data Analyst Interview Questions

44. how is joining different from blending in tableau.

45. What do you understand by LOD in Tableau?

LOD in Tableau stands for Level of Detail. It is an expression that is used to execute complex queries involving many dimensions at the data sourcing level. Using LOD expression, you can find duplicate values, synchronize chart axes and create bins on aggregated data.

46. Can you discuss the process of feature selection and its importance in data analysis?

Feature selection is the process of selecting a subset of relevant features from a larger set of variables or predictors in a dataset. It aims to improve model performance, reduce overfitting, enhance interpretability, and optimize computational efficiency. Here's an overview of the process and its importance:

Importance of Feature Selection:

- Improved Model Performance: By selecting the most relevant features, the model can focus on the most informative variables, leading to better predictive accuracy and generalization. - Overfitting Prevention: Including irrelevant or redundant features can lead to overfitting, where the model learns noise or specific patterns in the training data that do not generalize well to new data. Feature selection mitigates this risk. - Interpretability and Insights: A smaller set of selected features makes it easier to interpret and understand the model's results, facilitating insights and actionable conclusions. - Computational Efficiency: Working with a reduced set of features can significantly improve computational efficiency, especially when dealing with large datasets.

47. What are the different connection types in Tableau Software?

There are mainly 2 types of connections available in Tableau.

Extract : Extract is an image of the data that will be extracted from the data source and placed into the Tableau repository. This image(snapshot) can be refreshed periodically, fully, or incrementally.

Live : The live connection makes a direct connection to the data source. The data will be fetched straight from tables. So, data is always up to date and consistent. 

48. What are the different joins that Tableau provides?

Joins in Tableau work similarly to the SQL join statement. Below are the types of joins that Tableau supports:

• Left Outer Join
• Right Outer Join
• Full Outer Join

49. What is a Gantt Chart in Tableau?

A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project.

50. Using the Sample Superstore dataset, create a view in Tableau to analyze the sales, profit, and quantity sold across different subcategories of items present under each category.

• Load the Sample - Superstore dataset

• Drag Category and Subcategory columns into Rows, and Sales on to Columns. It will result in a horizontal bar chart.

• Drag Profit on to Colour, and Quantity on to Label. Sort the Sales axis in descending order of the sum of sales within each sub-category.

51. Create a dual-axis chart in Tableau to present Sales and Profit across different years using the Sample Superstore dataset.

• Drag the Order Date field from Dimensions on to Columns, and convert it into continuous Month.

• Drag Sales on to Rows, and Profits to the right corner of the view until you see a light green rectangle.

• Synchronize the right axis by right-clicking on the profit axis.

• Under the Marks card, change SUM(Sales) to Bar and SUM(Profit) to Line and adjust the size.

52. Design a view in Tableau to show State-wise Sales and Profit using the Sample Superstore dataset.

• Drag the Country field on to the view section and expand it to see the States.

• Drag the Sales field on to Size, and Profit on to Colour.

• Increase the size of the bubbles, add a border, and halo color.

From the above map, it is clear that states like Washington, California, and New York have the highest sales and profits. While Texas, Pennsylvania, and Ohio have good amounts of sales but the least profits.

53. What is the difference between Treemaps and Heatmaps in Tableau?

54. using the sample superstore dataset, display the top 5 and bottom 5 customers based on their profit..

• Drag Customer Name field on to Rows, and Profit on to Columns.

• Right-click on the Customer Name column to create a set

• Give a name to the set and select the top tab to choose the top 5 customers by sum(profit)

• Similarly, create a set for the bottom five customers by sum(profit)

• Select both the sets, right-click to create a combined set. Give a name to the set and choose All members in both sets.

• Drag top and bottom customers set on to Filters, and Profit field on to Colour to get the desired result.

Data Analyst Interview Questions On Python

55. what is the correct syntax for reshape() function in numpy .

56. What are the different ways to create a data frame in Pandas?

There are two ways to create a Pandas data frame.

• By initializing a list

• By initializing a dictionary

57. Write the Python code to create an employee’s data frame from the “emp.csv” file and display the head and summary.

To create a DataFrame in Python , you need to import the Pandas library and use the read_csv function to load the .csv file. Give the right location where the file name and its extension follow the dataset.

To display the head of the dataset, use the head() function.

The ‘describe’ method is used to return the summary statistics in Python.

58. How will you select the Department and Age columns from an Employee data frame?

You can use the column names to extract the desired columns.

59. Suppose there is an array, what would you do? 

num = np.array([[1,2,3],[4,5,6],[7,8,9]]). Extract the value 8 using 2D indexing.

Since the value eight is present in the 2nd row of the 1st column, we use the same index positions and pass it to the array.

60. Suppose there is an array that has values [0,1,2,3,4,5,6,7,8,9]. How will you display the following values from the array - [1,3,5,7,9]?

Since we only want the odd number from 0 to 9, you can perform the modulus operation and check if the remainder is equal to 1.

Become a Data Scientist with Hands-on Training!

61. There are two arrays, ‘a’ and ‘b’. Stack the arrays a and b horizontally using the NumPy library in Python.

You can either use the concatenate() or the hstack() function to stack the arrays.

62. How can you add a column to a Pandas Data Frame?

Suppose there is an emp data frame that has information about a few employees. Let’s add an Address column to that data frame.

Declare a list of values that will be converted into an address column.

63. How will you print four random integers between 1 and 15 using NumPy?

To generate Random numbers using NumPy, we use the random.randint() function.

64. From the below DataFrame, how will you find each column's unique values and subset the data for Age<35 and Height>6?

To find the unique values and number of unique elements, use the unique() and nunique() function.

Now, subset the data for Age<35 and Height>6.

65. Plot a sine graph using NumPy and Matplotlib library in Python.

Below is the result sine graph.

66. Using the below Pandas data frame, find the company with the highest average sales. Derive the summary statistics for the sales column and transpose the statistics.

• Group the company column and use the mean function to find the average sales

• Use the describe() function to find the summary statistics

• Apply the transpose() function over the describe() method to transpose the statistics

So, those were the 65+ data analyst interview questions that can help you crack your next data analyst interview and help you become a data analyst. 

Now that you know the different data analyst interview questions that can be asked in an interview, it is easier for you to crack for your coming interviews. Here, you looked at various data analyst interview questions based on the difficulty levels. And we hope this article on data analyst interview questions is useful to you. 

On the other hand, if you wish to add another star to your resume before you step into your next data analyst interview, enroll in Simplilearn’s Data Analyst Master’s program , and master data analytics like a pro!

Unleash your potential with Simplilearn's Data Analytics Bootcamp . Master essential skills, tackle real-world projects, and thrive in the world of Data Analytics. Enroll now for a data-driven career transformation!

1) How do I prepare for a data analyst interview? 

To prepare for a data analyst interview, review key concepts like statistics, data analysis methods, SQL, and Excel. Practice with real datasets and data visualization tools. Be ready to discuss your experiences and how you approach problem-solving. Stay updated on industry trends and emerging tools to demonstrate your enthusiasm for the role.

2) What questions are asked in a data analyst interview? 

Data analyst interviews often include questions about handling missing data, challenges faced during previous projects, and data visualization tool proficiency. You might also be asked about analyzing A/B test results, creating data reports, and effectively collaborating with non-technical team members.

3) How to answer “Why should we hire you for data analyst?”

An example to answer this question would be - “When considering me for the data analyst position, you'll find a well-rounded candidate with a strong analytical acumen and technical expertise in SQL, Excel, and Python. My domain knowledge in [industry/sector] allows me to derive valuable insights to support informed business decisions. As a problem-solver and effective communicator, I can convey complex technical findings to non-technical stakeholders, promoting a deeper understanding of data-driven insights. Moreover, I thrive in collaborative environments, working seamlessly within teams to achieve shared objectives. Hiring me would bring a dedicated data analyst who is poised to make a positive impact on your organization."

4) Is there a coding interview for a data analyst? 

Yes, data analyst interviews often include a coding component. You may be asked to demonstrate your coding skills in SQL or Python to manipulate and analyze data effectively. Preparing for coding exercises and practicing data-related challenges will help you succeed in this part of the interview.

5) Is data analyst a stressful job?

The level of stress in a data analyst role can vary depending on factors such as company culture, project workload, and deadlines. While it can be demanding at times, many find the job rewarding as they contribute to data-driven decision-making and problem-solving. Effective time management, organization, and teamwork can help manage stress, fostering a healthier work-life balance.

Find our Data Analyst Online Bootcamp in top cities:

About the author.

Shruti is an engineer and a technophile. She works on several trending technologies. Her hobbies include reading, dancing and learning new languages. Currently, she is learning the Japanese language.

Recommended Resources

Data Analyst Resume Guide

Data Analyst vs. Business Analyst: A Comprehensive Exploration

Data Scientist vs Data Analyst: Breaking Down the Roles

Business Intelligence Career Guide: Your Complete Guide to Becoming a Business Analyst

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

LIVE UPDATES

CPI Report: April Inflation Moderates, Sets Stage for Fed Rate Cuts

Follow news and analysis of april's data..

Last Updated: 

12 hours ago

Is Inflation Finally Cooling Again? It's the Fed's Big Question.

Megan Leonhardt

Economists expect the latest U.S. inflation data to show that price growth cooled slightly in April. That could help pave the way for interest-rate cuts later this year.

The consumer price index likely rose 3.4% in April compared with a year ago, economists surveyed by Factset estimate. That compares with a 3.5% increase in headline inflation in March. The Bureau of Labor Statistics is set to publish April’s CPI reading on Wednesday, May 15, at 8:30 a.m. Eastern.

Economists expect the April CPI report to show a third consecutive month of 0.4% price growth.

Core CPI, which excludes the more volatile food and energy components, is expected to show an increase of 3.6% year over year, down from the 3.8% rate logged in March. Economists anticipate that core price growth also moderated on a monthly basis, to 0.3% rate in April from a March pace of 0.4%.

The April data will be the last CPI reading before the Federal Reserve’s next policy meeting on June 11-12. The Fed’s preferred inflation gauge, the personal consumption expenditures price index, will publish data for April on May 31.

In addition to a slightly softer inflation reading, economists expect that wage growth remained steady last month. In March, average hourly earnings grew by 3.9% year over year, and that rate is expected to hold for April.

“Softening wage growth should help keep core service inflation at bay,” writes BeiChen Lin, investment strategist at Russell Investments.

While economists and Fed officials expect inflation to moderate further through the end of the year, Lin foresees “some hiccups now and then.” He is particularly watching for volatility in airfare pricing, for example, after some airlines announced cutbacks to their schedules.

The producer price index report for April was released May 14, ahead of the CPI data. It revealed that wholesale goods costs rose more than expected last month, up 0.5% for the month and higher than the 0.3% FactSet consensus.

Yet Federal Reserve Chair Jerome Powell called Tuesday’s PPI results “quite mixed,” pointing to the marked downward revisions to the prices received in March at the wholesale level. Bill Adams, chief economist for Comerica Bank, writes that the stronger-than-expected PPI report “suggests upside risk” to inflation.

Yet Tuesday’s PPI results are unlikely to change CPI expectations for April, Kurt Rankin, senior economist at PNC, tells Barron’s. “Producers’ costs act on consumer prices with a lag—meaning that the April 2024 PPI numbers will not impact CPI inflation until the summer months’ data releases,” Rankin says.

This relationship is cause for concern, however, particularly for Fed officials who are monitoring inflation trends to ensure that progress continues toward the central bank’s 2% target. Powell said Tuesday that Fed officials are “just going to have to see where the inflation data fall out” before making any adjustments to the current benchmark interest rate levels.

“Consumer price inflation has been staunchly supported by consumer demand throughout the past year, and now producers passing their own rising costs onto consumers looks to add to that upward pressure, diminishing the potential for easing consumer price inflation into the second half of the year,” Rankin says.

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