Interview Experience for the role of Data Scientist at Deloitte
1. G values, P values, T values
2. Conditional Probability
3. Central Values of Tendency
4. Can Linear Regression be used for Classification? If Yes, why if No why?
5. Hypothesis Testing. Null and Alternate hypothesis
6. Derivation of Formula for Linear and logistic Regression
7. Where to start a Decision Tree. Why use Decision Trees?
8. PCA Advantages and Disadvantages?
9. Why Bayes theorem? DB Bayes and Naïve Bayes Theorem?
10. Central Limit Theorem?
11. R packages in and out? For us it’s Python Packages in and out.
12. Scenario based question on when to use which ML model?
13. Over Sampling and Under Sampling
14. Over Fitting and Under Fitting
15. Core Concepts behind Each ML model.
16. Genie Index Vs Entropy
17. how to deal with imbalance data in classification modelling? SMOTHE techniques
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