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Preface

Part I. Introduction

1. Data Science
2. Jupyter Notebook Environment

Part II. Python Basics

3. Python Basics
- 3.1. Python Basics
4. Data Collections
5. Control Structures
- 5.3. Conditionals
- 5.4. Iteration
6. Functions

Part III. Working with Data

7. Relational Databases
- 7.1. SQLite Lab
- 7.2. MySQL Lab
8. NumPy Arrays
9. Pandas

Part IV. Data Visualization

10. Visualization
11. Pandas Visualization
12. Matplotlib Overview
13. Seaborn

Part V. Inferential Statistics

14. Probability and Statistics\n\nIntro content placeholder.
- 14.1. Descriptive Statistics & Distribution
- 14.2. Sampling Variability
15. Testing Hypothesis
16. Comparing Two Samples
17. Estimation
18. Prediction with Regression

Part VI. Machine Learning

19. Machine Learning
20. Multiple Regression
- 20.5. Linear Regression
- 20.6. Multiple Regression
21. Classification

.ipynb

Probability and Statistics\n\nIntro content placeholder.

14. Probability and Statistics\n\nIntro content placeholder.#

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13. Seaborn

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14.1. Descriptive Statistics & Distribution

By Tsangyao Chen

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