How To Tackle Data Science Interview Questions? Learn With Examples

An interdisciplinary field, data science encompasses a wide variety of topics. From mathematics to machine learning to programming skills, data science is a magnificent tree that has many branches. And when data science interview questions/ interviews come into the picture, it naturally gets tricky to wrap this vast realm into a single frame. Are you worried about how to answer the questions and carry on your data science interview preparation? 

Well, don’t worry, there is a way to get an extra edge and crack all data science questions thrown your way. And that is to prepare beforehand by practicing data science interview questions and answers. This is why we have prepared a list of a few frequently asked data science questions to help you visualize what the interview is going to be like. 

Mathematical Data Science Interview Questions:

A snail falls down a well 50ft deep. Each day it climbs up 3ft, and each night slides down 1ft. How many days does it take him to get out?

This is an example of a technical data scientist interview question. You need to be quick and accurate when answering such questions in an interview. The purpose of such questions could be to check whether or not you have the basic maths skills required for the job. These base skills form the foundation blocks for statistics and machine learning, and other complex topics. It is hence essential to let the recruiter know how efficient you are in solving these maths problems if you want to leave a good impression. 

You might also encounter puzzle questions, along similar lines. But note that the level of difficulty of the questions will vary as the interview proceeds further. A few examples of such data science interview questions are:

• You have a 10x10x10 cube, made of one thousand 1x1x1 cubes. If you remove the outer layer of this structure, how many cubes will you have left?
• A race track has 5 lanes. There are 25 horses and one would like to find out the 3 fastest horses of those 25. What is the minimum number of races one would need to conduct to determine the 3 fastest horses?

Statistical Questions:

What is the difference between a false negative and a false positive?

Statistics is considered one of the founding fathers of data science. Statistical questions often feature in data science interview questions. Remember that the questions like these need not be answered with exact definitions from the book. Rather answer by describing relevant scenarios where you can apply the questions, and then further discuss possible outcomes for the same. A few other data science interview questions based on similar grounds are:

• How would you explain linear regression to a business executive?
• What is the null hypothesis?
• What are the practical implications of the Central Limit Theorem?
• How do you calculate statistical power?

Coding/ Programming Questions:

How are the missing and impossible values represented in R?

While the fields mentioned above are of paramount importance, coding is at the heart of data science. You need to have a strong grip on at least one programming language if you want to get your desired job in the field. R programming, Python, and SQL are some of the primary languages in the domain of data science. So, questions regarding these languages might be incorporated into the interview. Here are a few samples of programming-related data science interview questions:

• How do you merge two data frames in R- Programming?
• Mention 3 key differences between Python and R.
• How can you do web scraping in Python?
• Which Python library would you prefer for Data wrangling?
• Write down a SQL script to return data from two tables.

What is the difference between supervised and unsupervised machine learning?

Being familiar with the concepts of machine learning is imperative for taking a leap into the world of data science. The interviewer might ask you to brief him about a few basic concepts of machine learning. You might even have to outline a given problem and pen down algorithms for the same. A few other examples of questions from the machine learning space, that are relevant for data science interviews are:

• Can you explain the Bias-Variance trade-off?
• How would you deal with sparse data?
• Tell me about a machine learning project you admire.

Practical Experience Questions:

Tell me about your first data science project.

Technical questions i.e. mathematical, statistical, programming, etc. are critical in gauging how suitable you are for the job. And while these have a high weightage in the segment of data science interview questions, the interviewer might also ask questions about your personal experience. For example about the projects you’ve done and other work experience. These questions shed light on your habits, abilities in general, and quality & pace of performance. Note that, when answering these questions do not clutter your answer by trying to incorporate multiple projects. Instead, choose a single project, preferably one that incorporates different skills, and explain your experience through that. A few examples of these questions are:

• Do you have experience in Tableau?
• Your preferred language is Python. But what experience do you have in R? (or vice-versa)

Behavioral Questions:

The behavioral questions are critical from the interviewer's POV, who is trying to judge how well you fit in the workplace environment or your teamwork abilities. What makes tackling these questions difficult is that there is no right or wrong answer. So be true to yourself, and answer the questions with what truly feels right for you. Here are a few samples of these questions to give you a clearer picture:

• Talk about a situation where you had to balance competing priorities.
• Describe a situation when you failed to meet a deadline.
• How did you motivate yourself when you were bored at work?

Case Study Questions:

This is another important section that forms a part of data scientist interview questions. Case study questions help the interviewer assess your skills beyond your technical knowledge, like logical thinking and problem-solving aptitude. Hence make sure to prepare for these as well. The trick behind answering these questions is noting that none of the answers are entirely wrong/ right, or that there may be multiple right answers to the questions. In most situations, the interviewer might not know the exact answer either! A few examples of such questions are:

• How many ping pong balls can fit into this room? 
• How many cars are parked downstairs?

The data science industry is booming and evolving at a rapid pace. Because of the vastness that this field and its fast pace, you need to be well-read and have a thorough understanding of the subject. Staying up-to-date is the cue to cracking data science interview questions, and landing the job. Also, ensure that you have a solid conceptual understanding and base knowledge of the topics covered above. If you do this, you will be all set for your first data science interview! All the best!

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