1. What Are the Feature Vectors?
  2. Explain the Steps in Making a Decision Tree.
  3. What Is Root Cause Analysis?
  4. What Is Logistic Regression?
  5. What Are Recommender Systems?
  6. Explain Cross-Validation.
  7. What Is Collaborative Filtering?
  8. Do Gradient Descent Methods at All-Time Converge to a Similar Point?
  9. What Is the Goal of A/B Testing?
  10. What Are the Drawbacks of the Linear Model?
  11. What Is the Law of Large Numbers?
  12. What Are Confounding Variables?
  13. Explain Star Schema.
  14. How Regularly Must an Algorithm be Updated?
  15. What Are Eigenvalue and Eigenvector?
  16. Why Is Resampling Done?
  17. Explain Selective Bias.
  18. What Are the Types of Biases That Can Occur During Sampling?
  19. Explain Survivorship Bias.
  20. How Do You Work Towards a Random Forest?

TOP MNC DATA SCIENCE INTERVIEW QUESTIONS