Job Interview Experience For The Role Of Data Scientist at Accenture

1. What is difference between K-NN and K-Means clustering?
2. How to handle missing data? What imputation techniques can be used?
3. Explain topic modelling in NLP and various methods in performing topic modeling.
4. Explain how you would find and tackle an outlier in the dataset.
5. Follow up: What about inlier?
6. Explain back propagation in few words and its variants?
7. Is interpretability important for machine learning model? If so, ways to achieve interpretability for a machine learning models?
8. Is interpretability important for machine learning model? If so, ways to achieve interpretability for a machine learning models?
9. How would you design a data science pipeline?
10. Explain bias – variance trade off. How does this affect the model?
11. What does a statistical test do?
12. How to determine if a coin is biased? Hint: Hypothesis testing 

Date: 28/06/21
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