Data Science Interview Questions

Photo by Markus Winkler on Unsplash
  1. Difference between Overfitting & Underfitting
  2. How to handle missing values in data?
  3. How to handle Imbalanced data?
  4. Difference between Covariance and Correlation.
  5. What are feature engineering and feature selections
  6. Difference between Normalization and Standardization
  7. What is PCA?
  8. Hyperparameter Tuning
  9. Difference between Linear and Logistics Regression.
  10. Explanation about Algorithms in your projects.
  11. What is Normal distribution?
  12. What is Multicollinearity and how do you handle it?
  13. Tell me about Central Limit Theorem.
  14. What is VIF?
  15. Explain Bias & Variance Trade-off?
  16. Precision and Recall Trade-off
  17. Difference between Bagging and Boosting.
  18. Explanation of Type-I & Type-II error
  19. Explain Lasso & Ridge?
  20. Difference between R2 and Adjusted R2
  21. What are MAE and RMSE?
  22. Explain K-Means clusters and the Elbow method.
  23. Explain AUC-ROC.
Photo by Markus Winkler on Unsplash
  1. Explain Activation Functions?
  2. Explain Optimizers?
  3. Difference between Sigmoid and Softmax.
  4. Difference between Vanishing and Explode Gradient Descent.
  5. Why Relu is used in Hidden Layers?
  6. How do we decide how many hidden layers and neurons should be taken?
  7. Architecture of CNN
  8. Difference between RNN and LSTM.

Conclusions

Thank You

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I am Data Scientist working in Cognizant | Writing about Data Science, AI, ML, DL, Stats, Math

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Jane Alam

Jane Alam

I am Data Scientist working in Cognizant | Writing about Data Science, AI, ML, DL, Stats, Math

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