1. Explain Eigenvalue and Eigenvector
  2. Define the term cross-validation
  3. Explain the steps for a Data analytics project
  4. Discuss Artificial Neural Networks
  5. What is Back Propagation?
  6. What is a Random Forest?
  7. What is the importance of having a selection bias?
  8. What is the K-means clustering method
  9. Explain the difference between Data Science and Data Analytics
  10. Explain p-value?
  11. Define the term deep learning
  12. Explain the method to collect and analyze data to use social media to predict the weather condition.
  13. When do you need to update the algorithm in Data science?
  14. What is Normal Distribution
  15. Which language is best for text analytics? R or Python?
  16. Explain the benefits of using statistics by Data Scientists
  17. Name various types of Deep Learning Frameworks
  18. What is skewed Distribution & uniform distribution?
  19. What is reinforcement learning?
  20. What is precision?

TOP MNC DATA SCIENCE INTERVIEW QUESTIONS