Sample Technical Interview Questions: Data Science

While looking for a new job in data science, there's a strong likelihood that you will be asked to complete a technical interview. These interviews provide you with an opportunity to show off your technical skills by answering theoretical or technical questions. In this post we've compiled a set of practice questions that you can use to prepare for the big day!
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Data science is a rapidly growing, interdisciplinary field that incorporates statistics, machine learning, and computer science fundamentals to help companies derive insights from raw data. Individuals in this role are expected to harness a variety of industry-standard tools and possess a deep background knowledge of modeling, statistics, and deep learning. 

To validate these skills, Data Science job candidates are often required to complete a technical interview. These interviews might consist of a mix of concept questions and practical problems one would encounter on the job. Demonstrating the application of fundamentals is key to landing any position.

To help you prepare for a Python interview, we’ve compiled a list of sample questions that will help you get a feel for what to expect.

Data Science Sample Technical Interview Questions 

1. What is the difference between supervised and unsupervised learning?

2. How does one build a random forest model?

3. Explain the difference between Data Science and data analytics, defining both terms. How is Data Science different from application programming?

4. Given plotted points, calculate the Euclidean distance in Python.

5. How do you find RMSE and MSE in a linear regression model?

6. What are time series problems? How do they differ from other regressions? 

7. Differentiate between the long and wide format data.

8. What is k-fold cross-validation?

9. Given a list of values as actual and predicted, write code to calculate the RMSE. 

10. What events would trigger you to update an algorithm? How regularly would you update it?

11. What is the bias-variance trade-off and how can it be applied to Data Science?

12. What is the importance of data cleaning and how would you undergo this process?

13. How are Data Science and Machine Learning related to each other?

14. How would you handle a dataset with 30% or more of its variables containing missing values? 

15. Given a confusion matrix, calculate precision and recall. 

16. Explain boosting, bagging, and stacking, in terms of their relation to Data Science?

17. What is the importance of dimensionality reduction?

18. What is p-value and how is it used in context?

19. Describe the problem-solving approach you take to a data-based project. Outline the steps you take in detail. 

20. How are false negatives and false positives both important? Provide an example for each.

Data Science Interview Preparations

Ready to learn more? Here are a few additional great sources for building your base of knowledge and continuing your preparation for a Data Science interview

This site offers a large collection of technical and theoretical practice problems across tech industry fields. Users can sort by category, like mathematical, strings, and programming, or even by the target company in order to best prepare for the type of questions they might encounter. 

Pramp is a free service that pairs optimal peers for 30-45 minute practice interviews over video. Both parties gain experience and feedback from one another, while the service provides full interview questions, answers, and tips. 

This site bills itself as “everything you need to crack your next tech interview,” with users gaining employment at companies like Facebook, Google, and Amazon. Their interview guides are sorted by companies and technologies, with 1:1 peer mock interviews also available. 

A data science community with more than 900,000 members, Analytics Vidhya offers immersive bootcamps, certification courses, and free introductory classes in topics like neural networks and Python. They’ve also written articles containing Data Science interview questions and answers. 

To find Data Science bootcamps, check out our overview page for programs across the country—both in-person and online. If you’re looking to dive into a specific subject area, such as machine learning or Python, view our subject pages. If you’re ready to start applying for Data Science jobs now, review out our Guide to Writing a Technical Resume to start creating your application materials. To learn more about technical interviews in general—what to expect, study tips, and more—read through our Guide to Acing the Technical Interview

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