1. Explain what regularization is and why it is useful.
  2. Which data scientists do you admire most? which startups?
  3. How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression.
  4. Explain what precision and recall are. How do they relate to the ROC curve?
  5. How can you prove that one improvement you've brought to an algorithm is really an improvement over not doing anything?
  6. What is root cause analysis?
  7. Are you familiar with price optimization, price elasticity, inventory management, competitive intelligence? Give examples.
  8. What is statistical power?
  9. Explain what resampling methods are and why they are useful. Also explain their limitations.
  10. What is selection bias, why is it important and how can you avoid it?
  11. What do you mean by word Data Science?
  12. Explain the term botnet?
  13. What is Data Visualization?
  14. Why data cleaning plays a vital role in analysis?
  15. What is Linear Regression?
  16. What do you understand by term hash table collisions?
  17. Compare and contrast R and SAS?
  18. What do you understand by letter ‘R’?
  19. What is the goal of A/B Testing?
  20. What is an Eigenvalue and Eigenvector?

TOP MNC DATA SCIENCE INTERVIEW QUESTIONS