1. What is Data Science?
2. What are the main components of Data Science?
3. What is the role of a Data Scientist?
4. Define Big Data in the context of Data Science.
5. What are structured and unstructured data?
6. What is data cleaning and why is it important?
7. What is a dataset in Data Science?
8. How do you define machine learning?
9. What is the difference between supervised and unsupervised learning?
10. What is a predictive model?
11. What is data wrangling?
12. What is the difference between data analysis and data mining?
13. Define feature engineering.
14. What is overfitting in machine learning models?
15. What is underfitting in machine learning models?
16. What is the purpose of cross-validation?
17. What is a training dataset?
18. What is a test dataset?
19. What is the difference between classification and regression?
20. Define a decision tree.
21. What is a random forest?
22. Explain the concept of a confusion matrix.
23. What are the accuracy, precision, and recall metrics?
24. What is an ROC curve?
25. What is the bias-variance tradeoff?
26. What is a neural network?
27. Explain what deep learning is.
28. What is the difference between AI, machine learning, and deep learning?
29. What are the most common programming languages used in Data Science?
30. What is the role of Python in Data Science?
31. What are NumPy and Pandas in Python?
32. What is a data frame?
33. How do you handle missing values in a dataset?
34. What is imputation in the context of data science?
35. Explain the concept of normalization.
36. What is standardization in data preprocessing?
37. What is a correlation in Data Science?
38. What is covariance?
39. Explain what a histogram is.
40. What is a scatter plot?