50 Data Science Interview Questions

I needed this list of questions when I was interviewing for data science or ML engineering roles. This list aim to help data science leaders interview DS/ML engineers and help DS/ML engineers to study what is important and ace their interviews.

1. What is the difference between supervised and unsupervised learning?
2. What is the difference between regression, classification, clustering and ranking?
3. What metrics will you use to evaluate a regression problem?
4. What does it mean to have low MAE and high MSE?
5. What metrics will you use to evaluate a classification model?
6. Why is accuracy a bad metric for classification?
7. How can you tackle data imbalance?
8. Can you describe a situation where precision is more important than recall and F-score?
9. Is F-score a statistically significant metric?
10. Can you explain what is the area under ROC Curve (AUC-ROC)?
11. Is AUC_ROC immune to data imbalance?
12. What is the area under the PR curve (AUC_PR) metric?
13. How will you measure the association/correlation between two numerical variables?
14. How will you measure the association/correlation…