15 Interview Questions Every Data Scientist Should Be Able to Answer

In 2012, Harvard Business Review called data scientist the “Sexiest Job of the 21st Century.” Indeed reports that the average annual salary for this highly in-demand and important role is $123,709.

In an increasingly digital and technology-driven society, data science is playing a crucial role. Every business and organization can use the help of professionals in the field to mine and evaluate data and information to inform and make decisions, from creating products customers really want to driving better marketing strategies to streamlining the organization’s operations.

Given that the role demands a specialized skill set and knowledge, it’s no surprise that it’s so well-paid and sought-after. However, if you’re interviewing for a data science position, you’ll need to prove you qualifications — and that’s no easy feat. So, how do you wow interviewers and prove that you’re the best person for the job? Take a look at some of the most common interview questions and sample answers and read our tips for preparing to get started.

How do I prepare for a data science interview?

A data science interview will assess your skills and knowledge in the field, including technical principles, algorithms, terminology, processes and procedures. You should review what you know about the field to be able to field technical questions.

As with any interview, you’ll also be asked non-technical questions about your background and education, soft skills, work style, previous roles, ability to collaborate and more. Make sure you spend some time reviewing and preparing responses to common interview questions for any position, too. Questions you might encounter include:

• Tell me about yourself.
• Why are you interested in this role?
• What makes you unique?
• What are your strengths and weaknesses?
• Why are you leaving your current role?
• What’s your work style?

In some cases, you’ll interview with multiple people, with data science professionals asking technical questions and another team member, manager or HR representative asking general questions to assess your personality, professionalism and fit with the organization. It can be helpful to practice with a friend or colleague who also works in the field since they can assess your responses from a professional’s perspective. 

What are the interview questions for data science?

Here is a sampling of 15 questions that you’ll tend to hear in an interview for a data scientist position. Of course, you’ll encounter different questions depending on the company, interviewer, specific role and other factors. We’ve also included sample answers to help inform your own, although you should use your own knowledge and experiences to shape your response.

1. How would you define data science?

Sample answer: “Data science incorporates processes, principles, procedures, algorithms and other tools to glean insights from collected data. Based on the data scientist’s conclusions from this information, they will help organizations make better and more informed decisions. It’s an important role for any business, no matter what the niche or industry, and can drive decision-making across many areas and departments.”

2. Explain the differences between supervised and unsupervised learning.

Sample answer: “Supervised and unsupervised learning both refer to machine learning. In supervised learning, the training data requires labeled data. Unsupervised learning involves drawing conclusions from unlabeled data. Moreover, supervised learning is used to predict how data will behave, while unsupervised learning is used to evaluate and analyze it. These are the major differences between the types of learning, although there are some other factors that differentiate them as well, including the methodology and tools used.”

3. Why do we perform A/B testing?

Sample answer: “A/B testing is an important method for evaluating the efficacy of two or more variables. For example, in a marketing email campaign, you might use an A/B test to compare different subject lines to see which one produces the highest open rate for the same email contents. It’s important because it helps people and organizations figure out the best strategies and tools for reaching their target audience.”

4. What is sampling?

Sample answer: “Data sampling describes a range of statistical techniques, such as probability and cluster sampling, that’s used for collecting and analyzing representative data. It’s an important part of the research process that will help you evaluate the accuracy of your results later on.”

5. Which programming languages do you use most frequently?

Sample answer: “There are many important languages used in data science, such as Python, R, Scala, Java, SQL, Julia and others. While I’m experienced in using all of these languages, for this purpose, I tend to use Python most frequently.” 

(Note: You may be asked to take a test assessing your knowledge of programming languages most commonly used in data analysis. We’ll describe data science tests in greater detail below.)

6. Explain selection bias.

Sample answer: “Selection bias is an error that occurs when the data sampled is not representative of the population being analyzed. The professional excludes certain data from a study and analysis — meaning the chosen sets are not accurately reflective of being studied which results in the evaluation of the information and conclusions drawn from it being distorted and potentially inaccurate.”

7. What is root cause analysis and why is it important?

Sample answer: “Root cause analysis is a problem-solving process that means identifying the underlying (or root) causes of an issue or flaw. Basically, it involves asking ‘why?’ five times (more or fewer, depend on the circumstances) until you find the source of the issue. It’s important to figure out the root cause of what’s causing a problem to occur because removing it will likely resolve the issue and allow you to achieve the desired outcome. It can also help you prevent the problem from occurring in the future. It’s used in a wide range of industries and fields, including healthcare, project management, IT and others.”

8. How do you prevent overfitting?

Sample answer: “Overfitting means that the model is too complex or restrictive, confined to a small amount of data and resulting in an inaccurate predictive model. Regularisation methods are one way to prevent overfitting from occurring. You should also try to simplify the model to keep it from becoming too complex or ‘noisy.’”

9. How does central limit theorem apply to data science?

Sample answer: “Central limit theorem stipulates that when more data or variables are added to a sufficiently large set of data, the averages of the set will result in a normal distribution that can be depicted by a bell-shaped or Gaussian curve. This enables the data scientist to make inferences about the data, estimating the probability of samples deviating from the norm.”

10. Why do we perform data cleaning?

Sample answer: “While data cleaning can be challenging, especially as the sources of data are added, it’s a necessary process in data science. Data cleaning not only translates the sets into a format we can use for our analysis and evaluation, but it increases the accuracy of the analysis as well. Inaccurate, incomplete, irrelevant and otherwise faulty data can corrupt the entire model, after all.”

11. Describe machine learning and how it relates to data science.

Sample answer: “Machine learning is the process of enabling computers to ‘learn’ from data and change it’s processes and procedures accordingly. Data scientists use it to build algorithms and models to help them make predictions about how data will perform and behave, otherwise known as predictive analytics. Machine learning is important across disciplines and has the potential to address a wealth of complex problems more efficiently — and even more accurately — than the human mind is capable of doing.”

12. What are cluster sampling and systematic sampling?

Sample answer: “Cluster sampling is a technique of using clusters — collections of subjects or variables — to perform an analysis. It’s used when random sampling won’t result in a representative cross-section of the entire target population. Systematic sampling is another method that’s used when simple random sampling may not be effective. It involves selecting data in a systematic way through fixed intervals but using a randomly-chosen starting point. Equiprobability or equal probability is one method that falls into the category of systematic sampling.”

13. What’s the role of a decision tree in data science?

Sample answer: “A decision tree is a machine learning algorithm used for regression, classification and other supervised learning categories. It breaks down data and presents it in usable components for laypeople to understand. It allows data to be presented visually, with leaves representing classes or probabilities and internal nodes labeled with inputs.”

14. How do you deal with outlier values?

Sample answer: “If an outlier is truly incorrect, it can be eliminated. Otherwise, you might try a range of methods, such as changing the value of the outlier, using a transformation of the data or normalizing the data.”

15. How would you work with others on the data science team?

Sample answer: “I understand that this process isn’t a one-person operation, and I value teamwork and collaboration. Early on, I would try to get to know the other team members, including fellow data scientists and people in other roles, too. I’d ask them about their roles and how the team functions together currently. I want to make sure I understand the process and operations you have in place so I’m not disrupting the flow. I also want to work with others as opposed to alongside them, meaning I’d keep the lines of communication open and be involved in making everything from the data collection process to strategy refinement more efficient for everyone.”

What is a basic data science test?

A data science test is often used as part of the hiring process for data scientist candidates to evaluate their skills and ability to collect, evaluate and analyze data and draw conclusions from it to help organizations make better decisions. Often, it’s deployed early in the interview stages to assess whether the candidate has the minimum knowledge and competencies to perform the role in question. In order to prepare for a data science test, you can try one of the many practice tests available online, as well as use the techniques described above for preparing for a data science interview.

Final tips.

Want to ace your data science interview? The above questions will help you prepare and get an idea of what to expect. Keep these tips in mind to ensure that you’re ready to go on interview day, too.

Remember interview tips for all professionals.

...such as dressing professionally and bringing several copies of your resume.

Research the organization.

While your skills as a data scientist are important, so is demonstrating that you’re a good fit for the organization.

Don’t over-rehearse.

If you memorize answers or over-practice, you’ll come across stilted and robotic. 

Don’t be too anxious.

You know your stuff!

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