1. How can you assess a good logistic model?
  2. What are various steps involved in an analytics project?
  3. During analysis, how do you treat missing values?
  4. Explain about the box cox transformation in regression models
  5. Can you use machine learning for time series analysis?
  6. Write a function that takes in two sorted lists and outputs a sorted list that is their union.
  7. What is Regularization and what kind of problems does regularization solve?
  8. What is multicollinearity and how you can overcome it?
  9. What is the curse of dimensionality?
  10. How do you decide whether your linear regression model fits the data?
  11. What is the difference between squared error and absolute error?
  12. What is Machine Learning?
  13. How are confidence intervals constructed and how will you interpret them?
  14. How will you explain logistic regression to an economist, physican scientist and biologist?
  15. How can you overcome Overfitting?
  16. Differentiate between wide and tall data formats?
  17. Is Naïve Bayes bad? If yes, under what aspects.
  18. How would you develop a model to identify plagiarism?
  19. How will you define the number of clusters in a clustering algorithm?
  20. Is it possible to perform logistic regression with Microsoft Excel?

TOP MNC DATA SCIENCE INTERVIEW QUESTIONS