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Part I: Exploring Data

1. What is Data Science?
2. Data Science Case Study
3. Programming in Python
4. Collections of Data
5. Randomness and Control Statements
6. DataFrames
7. DataFrame Methods and Operations
8. String Data and Fuzzy Matching
9. Data Visualization
10. Data Collection
11. Probability
12. Empirical and Probability Distributions
13. Hypothesis Testing
14. Estimation and Confidence Intervals
15. Ethics and Pitfalls in Data Science
16. Traffic Stops Case Study
- 16.1. Study Background
- 16.2. Investigating Traffic Stops

Part II: Using Data to Understand Our World

17. Prediction and Correlation
- 17.1. Prediction
- 17.2. Correlation
18. 18. Simple Linear Regression
19. Multiple Linear Regression
20. Feature Engineering and Feature Selection
- 20.1. Feature Engineering
- 20.2. Feature Selection
21. Regularization and Cross Validation
22. Classification with Logistic Regression
23. Classification with Nearest Neighbors
24. Neural Networks and Deep Learning
25. Tree Models
26. Unsupervised Learning and Clustering
27. Data Wrangling
28. Relational Databases and SQL
29. Reproducibility
30. Case Study

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By the University of Chicago