1. Can you enumerate the various differences between Supervised and Unsupervised Learning?
  2. What do you understand by the Selection Bias? What are its various types?
  3. Please explain the goal of A/B Testing.
  4. How will you calculate the Sensitivity of machine learning models?
  5. Could you draw a comparison between overfitting and underfitting?
  6. Between Python and R, which one would you pick for text analytics and why?
  7. Please explain the role of data cleaning in data analysis.
  8. What do you mean by cluster sampling and systematic sampling?
  9. Please explain Eigenvectors and Eigenvalues.
  10. Can you compare the validation set with the test set?
  11. What do you understand by linear regression and logistic regression?
  12. Please explain Recommender Systems along with an application.
  13. Could you explain how to define the number of clusters in a clustering algorithm?
  14. What do you understand by Deep Learning?
  15. How does Backpropagation work? Also, it state its various variants.
  16. What do you know about Autoencoders?

TOP MNC DATA SCIENCE INTERVIEW QUESTIONS