100+ Common Data Science Interview Questions

Preparing for an interview is not easy – naturally there is a large amount of uncertainty regarding the data science interview questions you will be asked. No matter how much work experience or technical skill you have, an interviewer can throw you off with a set of questions that you didn’t expect.

For a data science interview, an interviewer will ask questions spanning a wide range of topics, requiring strong technical knowledge and communication skills from the part of the interviewee.

Your statistics, programming, and data modeling skills will be put to the test through a variety of questions and question styles – intentionally designed to keep you on your feet and force you to demonstrate how you operate under pressure. Preparation is a major key to success when in pursuit of a career in data science career in data science.

This guide contains all of the data science interview questions an interviewee should expect when interviewing for a position as a data scientist.

We set off to curate, create and edit different data science interview questions and provided answers for some. From this list of data science interview questions, an interviewee should be able to prepare for the tough questions, learn what answers will positively resonate with an employer, and develop the confidence to ace the interview. We’ve broken the data science interview questions into six different categories: statistics, programming, modeling, behavior, culture, and problem-solving.

1. Statistics
Statistical computing is the process through which data scientists take raw data and create predictions and models backed by the data. Without an advanced knowledge of statistics it is difficult to succeed as a data scientist – accordingly it is likely a good interviewer will try to probe your understanding of the subject matter with statistics-oriented data science interview questions. Be prepared to answer some fundamental statistics questions as part of your data science interview.
Here are examples of rudimentary statistics questions we’ve found:
What is the Central Limit Theorem and why is it important?
What is sampling? How many sampling methods do you know?
What is the difference between Type I vs Type II error?
What is linear regression? What do the terms P-value, coefficient, R-Squared value mean? What is the significance of each of these components?
What are the assumptions required for linear regression? — There are four major assumptions: 1. There is a linear relationship between the dependent variables and the regressors, meaning the model you are creating actually fits the data, 2. The errors or residuals of the data are normally distributed and independent from each other, 3. There is minimal multicollinearity between explanatory variables, and 4. Homoscedasticity. This means the variance around the regression line is the same for all values of the predictor variable.
What is a statistical interaction?
What is selection bias?
What is an example of a dataset with a non-Gaussian distribution?
What is the Binomial Probability Formula?

2. Programming
To test your programming skills, employers will ask two things during their data science interview questions: they’ll ask how you would solve programming problems in theory without writing out the code, and then they will also offer whiteboarding exercises for you to code on the spot.

2.1 General
With which programming languages and environments are you most comfortable working?

What are some pros and cons about your favorite statistical software?

Tell me about an original algorithm you’ve created.
Describe a data science project  in which you worked with a substantial programming component.