20 Data Scientist Interview Questions and Answers

Learn what skills and qualities interviewers are looking for from a data scientist, what questions you can expect, and how you should go about answering them.

A data scientist is a professional who extracts insights from data to solve business problems. To become a data scientist, you need to be proficient in statistics, machine learning, and programming. But before you can tackle big data problems, you need to ace the interview.

In this article, you will find data scientist interview questions and answers that will help you prepare for your interview.

Why do you want to work as a data scientist?

This question can help the interviewer get to know you better and understand why you are passionate about this career. Your answer should reflect your genuine interest in data science, as well as how it fits into your overall goals.

Example: “I have always been interested in computers and technology, so I started learning coding when I was a teenager. When I got to college, I took an introductory data science class and fell in love with the subject matter. I realized that I could use my coding skills to solve real-world problems using data analysis. This inspired me to pursue a master’s degree in data science, which led to my current job search.”

What are your greatest strengths and how would they help you in this role?

Employers ask this question to learn more about your personality and how you would fit into their company culture. When answering, it can be helpful to think of a few specific examples that relate to the job description. This can help show that you have researched the role and understand what is expected of you.

Example: “I am an extremely organized person who loves data analysis. I find that my greatest strengths are in finding patterns within large amounts of data and creating reports based on those findings. In my last position, I helped create a new system for tracking customer complaints by analyzing our current database. My ability to analyze information led me to discover that we were losing customers because of shipping errors. By discovering these patterns, I was able to make changes to improve our shipping process.”

Where did you get your education and training?

Employers ask this question to learn more about your background and how you got into the field of data science. They want to know if you have a degree in computer science or statistics, but they also want to see that you’ve gained experience through other means. When answering this question, be sure to include any relevant certifications you may have as well as any training courses you took.

Example: “I graduated from the University of California with a bachelor’s degree in mathematics. I then went on to get my master’s degree in statistics from the same university. While completing my undergraduate studies, I was able to take several classes that helped me understand data science better. After graduating, I enrolled in an online course for data scientist certification.”

What data scientists do you most admire?

This question can help the interviewer learn more about your background and experience in data science. You may choose to answer this question by naming a specific person or describing what you admire about several different people who work as data scientists.

Example: “I most admire my former professor, Dr. Smith. She was always willing to meet with me one-on-one to discuss my projects and offer advice on how I could improve my work. She also helped me find internships that led to my current position as a data scientist. Another data scientist I admire is John Doe. He has been working as a data scientist for many years, so he knows all of the best practices when it comes to analyzing data.”

What programming languages do you know best?

This question can help the interviewer determine your level of expertise with data science programming languages. You should list any that you are familiar with and explain why they’re important to you or how you use them in your work.

Example: “I am most comfortable using Python, R and SQL. I find these three languages to be the most useful for my projects because they allow me to analyze large amounts of data quickly and efficiently. In my last role, I used Python to create a machine learning algorithm that analyzed customer purchasing patterns and determined which products were likely to sell well together. This allowed us to make more informed decisions about our inventory.”

What’s your experience with SQL?

SQL is a programming language used to store and retrieve data. It’s an essential skill for any data scientist, so interviewers may ask this question to see if you have the necessary experience with SQL. In your answer, explain what SQL is and how you use it in your daily work. If you don’t have much experience with SQL, consider explaining why that’s the case and what other languages you’re familiar with.

Example: “I’ve been using SQL since I started my career as a data scientist. I find it to be one of the most useful tools when working with large amounts of data because it allows me to quickly sort through information and create reports. I also like that it’s easy to learn and understand, which makes it accessible to anyone who needs to use it.”

How would you test if survey responses were filled at random as opposed to truthful selections?

This question tests your ability to use data analysis techniques to test survey results. Use examples from past experience where you used similar methods to analyze data and ensure the integrity of a survey’s results.

Example: “In my last role, I was tasked with analyzing survey responses for a client who wanted to know if their customers were satisfied with their product. The company had recently changed its packaging, so we needed to see if customer satisfaction increased or decreased after the change. To test whether the survey responses were truthful, I created a control group that received the old packaging and another group that received the new packaging. Then, I compared the two groups’ survey responses to determine if there was an increase in customer satisfaction.”

Can you explain the concept of data mining?

This question is an opportunity to show your knowledge of the field. You can define data mining and explain how it’s used in a business setting.

Example: “Data mining is the process of analyzing large amounts of data to find patterns, trends or other useful information. It’s important for businesses because it allows them to make better decisions based on customer behavior. For example, if you’re running a clothing store, you could use data mining to analyze what products customers are buying together. This would allow you to create more effective marketing campaigns that target specific groups of people.”

What’s the difference between structured and unstructured data?

This question tests your knowledge of data types and how they’re used in the workplace. It also shows that you understand the importance of differentiating between them when analyzing information. When answering this question, define each type of data and explain their differences.

Example: “Structured data is organized into tables or lists with rows and columns. This makes it easy to analyze because I can use a database to store and organize it. Unstructured data is more complex because it’s not stored in a table format. Instead, it’s usually found in documents, emails, images and videos. To analyze unstructured data, I need to first convert it into structured data.”

What tools and technologies are you most comfortable using?

This question can help the interviewer determine your comfort level with various tools and technologies. You should highlight those that you are most comfortable using, but also include a few others that you have experience with to show versatility.

Example: “I am most comfortable working with Python and R, as I’ve used both for several years now. However, I also have some experience with SQL, Hadoop and Spark, which I learned through my previous job. I’m always looking to learn new things, so I would be open to learning any of these if needed.”

How do you stay up to date on the latest news and trends in the industry?

Employers want to know that you are passionate about your work and eager to learn more. They also want to see that you have the time management skills necessary to keep up with industry news while still completing your daily tasks. Show them how you stay informed by mentioning a few of your favorite sources for data science news and explaining why they’re important to you.

Example: “I subscribe to several blogs and newsletters, including Data Science Weekly and The Big Data Gazette. I find these resources helpful because they provide me with new insights into the field and help me discover new tools and techniques. I also like to attend conferences and webinars where I can meet other professionals in the industry.”

Are you comfortable presenting your findings to large groups?

This question can help the interviewer determine how comfortable you are with public speaking and whether or not you would be able to present your findings to a large group of people. If you have experience presenting in front of groups, share that information with the interviewer. If you do not have any public speaking experience, explain what steps you would take to prepare for such an event.

Example: “I am very comfortable presenting my findings to large groups. In fact, I have presented my data analysis to several different audiences throughout my career. When preparing for these presentations, I first make sure that all of my data is organized and easy to understand. Then, I create a presentation using software like Microsoft PowerPoint. Finally, I practice my delivery multiple times until I feel confident.”

Describe a time when you made a mistake at work and what you did to correct it.

This question can help the interviewer determine how you respond to challenges and learn from your mistakes. Use examples of times when you made a mistake, but also used them as learning opportunities to show that you’re eager to improve yourself.

Example: “In my previous role, I was tasked with creating a new algorithm for our company’s website. I worked on it all week and presented it to my team at the end of the week. However, after presenting it, I realized there were some errors in the code. I spent the rest of the weekend fixing the errors and re-presented it to my team the following Monday. While this may seem like a lot of work in one week, it helped me realize that I needed to be more thorough when testing my algorithms before presenting them.”

Have you ever had a disagreement with a coworker? Describe how you handled it.

This question can help interviewers understand how you work with others and resolve conflicts. Use your answer to show that you’re a team player who values collaboration.

Example: “In my last role, I disagreed with one of my coworkers about the best way to analyze data for a client project. Rather than speaking up in front of everyone, I waited until we were alone to discuss it with her. She appreciated my discretion, and we talked through our different approaches. We decided to try both methods and compare the results. In the end, we found that my approach was more effective.”

What is cross-tabulation and why is it important?

This question tests your knowledge of data analysis and how it can be used to solve problems. It also shows the interviewer that you understand the importance of cross-tabulation in relation to other types of data analysis. In your answer, define what cross-tabulation is and explain why it’s important for a data scientist to know about this process.

Example: “Cross-tabulation is when two or more variables are analyzed together. For example, if I wanted to analyze gender and age groups within my target audience, I would use cross-tabulation to do so. This type of analysis is very important because it allows me to see patterns between different factors. These insights help me make better decisions as a data scientist.”

What’s the difference between business intelligence and data science?

This question is a great way to test your knowledge of the two fields and how they differ. Your answer should include what each field entails, as well as why you chose data science over business intelligence.

Example: “Business intelligence focuses on collecting and analyzing data for businesses to make informed decisions. Data scientists use their expertise in mathematics, statistics and computer science to find insights that can help companies improve their operations. Business intelligence is more about using data to support current strategies while data science is more about discovering new ways to solve problems.”

Describe the nature of predictive analytics and which types you prefer to use.

This question is a great way to test your knowledge of the field and how you apply it. You can use this opportunity to show that you understand what predictive analytics are, as well as which types you prefer to use in your work.

Example: “Predictive analytics is the process of using data to predict future outcomes. There are many different types of predictive analytics, but I find that time series analysis is one of the most useful because it allows me to analyze historical data to make predictions about future events. This type of predictive analytics has helped me identify trends in customer behavior and develop strategies for improving sales.”

What do you know about natural language processing?

This question is a great way to test your knowledge of data science and the skills you need for this role. Use examples from your experience or education to show that you understand what natural language processing is, how it works and why it’s important.

Example: “Natural language processing is a type of artificial intelligence where computers can interpret human language. This technology has many applications in business because it allows machines to read documents and reports written by humans. In my last position, I used natural language processing to analyze customer feedback on social media. The results helped us create new products based on consumer preferences.”

Which metrics would you want to include in a dashboard for a new feature?

This question is a great way to show your analytical skills and how you can use data to make decisions. When answering this question, it’s important to be specific about the metrics you would include in a dashboard and why they’re important.

Example: “I would want to include all of the key performance indicators that are relevant to the new feature. For example, if I were creating a dashboard for a social media platform, I would include engagement rates, user retention rates and customer satisfaction rates. These three metrics are essential to understanding whether or not a new feature is successful.”

How would you go about cleaning data?

This question is an opportunity to show your technical skills and knowledge of data science. It’s a good idea to explain how you would clean the data, but it can also be helpful to mention what tools you use for cleaning.

Example: “I would start by looking at the type of data I have and then decide which method will work best. For example, if I had categorical variables that were nominal or ordinal, I would use methods like dummy coding or recoding. If I had numerical variables that were continuous, I would use methods like mean-centering and scaling. Another way I would clean data is through feature selection, where I remove irrelevant features from my dataset.”


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