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Part I. Fundamentals

1. Introduction
- 1.1. Data Science
- 1.2. On Programming
2. Python Basics

Part II. Working with Data

3. NumPy Arrays
- 3.1. Arrays Basics
- 3.2. Array Computation
4. Pandas

Part III. Data Visualization

5. Visualization
- 5.1. Pandas Visualization
6. Matplotlib Overview
7. Seaborn

Part IV. Inferential Statistics

8. Probability
- 8.1. Distribution
- 8.2. Sampling Variability
9. Testing Hypothesis
10. Two Samples
11. Estimation
12. Regression

Part V. Machine Learning

13. Multiple Regression
- 13.5. Linear Regression
- 13.6. Multiple Regression
14. Classification
15. K-Means Clustering

Appendices

16. Tooling
17. Work Environment
18. Bibliography

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By Tsangyao Chen

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