Data Science Interview Questions

1. What is data science and how does it differ from data analytics and machine learning?

2. What is the difference between supervised, unsupervised, and reinforcement learning?

3. What is the bias-variance tradeoff in data science?

4. What is overfitting and how do you prevent it?

5. What is the difference between correlation and causation in data analysis?

6. What is feature engineering and why is it important?

7. What is the difference between a population and a sample in statistics?

8. What is a p-value and how do you interpret it?

9. What is the difference between Type I and Type II errors?

10. What is the Central Limit Theorem and why does it matter for data science?

11. What is the difference between mean, median, and mode, and when do you use each?

12. What is standard deviation and how does it differ from variance?

13. What is a confidence interval and how do you interpret it?

14. What is hypothesis testing and what are the null and alternative hypotheses?

15. What is the difference between classification and regression problems?

16. What are precision, recall, and F1-score, and when do you use each?

17. What is a confusion matrix and what does it tell you?

18. What is the ROC curve and what does AUC represent?

19. What is cross-validation and why is it used?

20. What is the difference between L1 and L2 regularization?

21. What is dimensionality reduction and how does PCA work?

22. What is the curse of dimensionality in data science?

23. How do you handle missing data in a dataset?

24. How do you handle imbalanced datasets in classification?

25. What is the difference between bagging and boosting in ensemble learning?

26. What is A/B testing and how do you design a valid experiment?

27. What is the difference between a data warehouse and a data lake?

28. What is normalization vs standardization of features and when do you use each?

29. What is exploratory data analysis (EDA) and what does it involve?

30. What is the difference between batch and online learning in data science?

31. What is Data Science?

32. Data Scientist vs Data Analyst: What's the Difference?

33. What is Exploratory Data Analysis (EDA)?

34. What is Feature Scaling?

35. What is a P-Value?

36. What is Correlation?

37. What is a Normal Distribution?

38. What is Sampling in Statistics?

39. What is a Hypothesis Test?

40. What Is Regression in Data Science?

41. What Is ETL (Extract, Transform, Load)?

42. What Is Data Wrangling?

43. What Is a DataFrame?

44. What Is Imputation in Data Science?

45. What Is Standardization in Data Science?

46. What Is Dimensionality Reduction?

47. What Is a KPI (Key Performance Indicator)?

48. How Does Data Leakage Creep Into a Cross-Validation Pipeline?

49. Why Do Random K-Fold Splits Mislead on Time-Series Data?

50. How Do You Decide the Sample Size an Experiment Needs?

51. How Do You Handle the Multiple Comparisons Problem?

52. How Do You Choose an Evaluation Metric for a Rare Event?

53. What Is the Difference Between Calibration and Discrimination?

54. How Do You Reason About Confounding When You Cannot Run an Experiment?

55. What Are the Most Common Ways an A/B Test Result Turns Out to Be Wrong?

56. How Does the Missing-Data Mechanism Change Your Imputation Choice?

57. What Is Training/Serving Skew and How Does a Feature Store Reduce It?

58. How Do You Detect and Respond to Drift in a Deployed Model?

59. How Would You Explain a Model to a Non-Technical Stakeholder?

60. What Makes a Data Science Analysis Reproducible?

61. How Do You Tell a Stakeholder the Data Cannot Answer Their Question?