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Top 35 frequently asked Data Science interview questions

Interviews are very stressful. We should prepare for the worse. So, we have to plan accordingly in order to crack them. In this blog, you will get to know the type of questions that will be asked during the interview. It also depends on the experience level and the company too. This blog is mainly focused on entry-level Data Science related jobs.

If you haven’t read my previous blog-posts, I highly recommend you to go through them:
  1. Skills required to become a Data Scientist
  2. How to apply for a Data Science job?
First of all, you must be thorough with your resume, mainly your Internship experience and academic projects. You will have at least one project discussion round. Take mock interviews and improve your technical and presentation skills, which will surely help in the interviews.

Based on my experience, I have curated the topmost 35 frequently asked Data Science questions during the interviews.

  1. Explain the Naive Bayes classifier?
  2. In case of Regression, how do you split the nodes of a decision tree?
  3. In case of Classification, how do you split the nodes of a decision tree?
  4. How do you calculate the Gini Index/Information gain? How do you select the feature based on that?
  5. What is the difference between over-fitting and under-fitting?
  6. Explain the over-fitting in decision trees and how to overcome it?
  7. What is the difference between the decision tree and random forest?
  8. How to avoid overfitting in the random forest?
  9. Explain pruning? How does it work?
  10. What is the difference between pre-pruning and post-pruning?
  11. What is the difference between bagging and boosting?
  12. How logistic regression can be used? Explain the whole algorithm? Is it a linear or nonlinear boundary? How can we classify nonlinear data points?
  13. What is the log-loss function and how does it work?
  14. Can we use Mean Squared Error(MSE) instead of log-loss for a classification problem? Explain the reason.
  15. How to find k in k-means clustering?
  16. How does k effect overfitting and underfitting in KNN?
  17. What is the use of dimensionality reduction? When it can be used?
  18. How does Support vector machine (SVM) work?
  19. Describe the algorithm Principal Component Analysis(PCA) and the mathematics behind it?
  20. How does gradient descent work?
  21. What is the difference between Batch and Stochastic gradient descent?
  22. How can we improve the performance of a complex architecture model?
  23. What are the different ways of imputing null values in a high cardinality categorical feature?
  24. What are the properties of the covariance matrix?
  25. What is the difference between covariance and correlation matrix?
  26. What is the difference between precision and recall and how to calculate them?
  27. Why F1-score is the harmonic progression of Precision and Recall? How does it change w.r.t Precision and Recall?
  28. Explain L1 and L2 regularization and their difference?
  29. What is the R-squared metric? How to interpret it?
  30. Which model will you choose: A model with high precision and low recall or A model with low precision and high recall. Explain your thoughts.
  31. Which metrics can be used to compare the performance of text based output, in Machine Transalation and Image captioning? Hint: This metric is used mostly in NLP tasks.
  32. Why LSTM is prefered over RNN?
  33. What is the vanishing gradient problem and how do you overcome it?
  34. What is the advantage of using dropout layer?
  35. What is data normalization and why do we need it?
Hope, you got a clear idea after reading this blog. If you have any queries, comment in the comments section below. I would be more than happy to answer your queries. I will create a separate post, which will have all the answers for these questions and will post them in one of my future posts.
Thank you for reading my blog and supporting me. Stay tuned for my next post. If you want to receive email updates, don’t forget to subscribe to my blog. Keep learning and sharing!!

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  1. Worth Reading it. Thank you Abhishek M

  2. Can you please share the answers too.

  3. This comment has been removed by a blog administrator.

  4. If you don’t know the answers to these questions, then you don’t even have to spend time to go for an interview, but rather prepare for it and increase your level of knowledge.

  5. The frequently asked data science interview questions are very smart. Thanks a lot for the article published so far.

  6. Hi Abhishek, It is a good read and nice collection of questions.

  7. Thank you for sharing your experience. Cracking the coding interview can seem a daunting task, but learning and practicing the right thing repeatedly can be helpful. The set of questions you have shared are helpful and gives clear idea of interview process.

  8. You have written impressive tutorials on data science, I really commend your work. This is one of the best blogs to learn data science, thank you for sharing your journey. Keep sharing your valuable knowledge and expertise. Looking forward to learn more, great blog. Data science training in Chennai

  9. This comment has been removed by a blog administrator.

  10. I must state that the blog post is really beneficial to anyone else who reads it because the information and knowledge it includes is vital. Continue to provide such useful knowledge through your posts and keep posting more on
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  12. You shared valuable science questions for the preparation of the interview. The most important thing to grab a job is sound preparation for the interview. If candidates prepare well then they will feel much more in control. They will have anticipated what will get asked in the interview, and they will have learned so much about the business and job profile so naturally they’ll feel cool, calm, and collected. Also, if they know about the job profiles and future opportunities it becomes easier for them to navigate towards success. Thanks for sharing such valuable tips for job interviews.

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