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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
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

1. Work Environment
2. Cheat Sheets
3. Work Environment
4. Bibliography

Repository
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Cheat Sheets

Contents

2.1. Math Definitions in Data Science
2.2. Matrices

2. Cheat Sheets#

2.1. Math Definitions in Data Science#

../../_images/math_definitions_data_science.jpeg — Fig. 2.6 Mathematical Definitions in Data Science #

2.2. Matrices#

../../_images/matrices.jpeg — Fig. 2.7 Matrices #

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Contents

2.1. Math Definitions in Data Science
2.2. Matrices

By Tsangyao Chen

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