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Introduction to Data Science I & II

Introduction

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. Simple Linear Regression
19. Multiple Linear Regression
20. Relational Databases and SQL
21. Data Warehouses and Data Lakes
22. Scalable Data Processing
23. Classification
24. Clustering
25. Feature Engineering and Feature Selection
26. Prediction with Many Features
27. Neural Networks
28. Cloud Computing
29. Reproducibility

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