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Part I. Introduction

  • 1. Data Science
  • 2. Working Environment
    • 2.1. The CLI
    • 2.2. Python installation
    • 2.3. Virtual Environment
    • 2.4. Jupyter Notebook
    • 2.5. Launch Jupyter FAST!

Part II. Python Basics

  • 3. Python Basics
  • 4. Data Collections
    • 4.1. Lists
    • 4.2. Dictionaries
    • 4.3. Tuples
  • 5. Control Structures
    • 5.1. Conditionals
    • 5.2. Iteration
  • 6. Functions

Part III. Working with Data

  • 7. Relational Databases
    • 7.8. SQLite Lab: Hands-on with RDB
    • 7.9. MySQL Lab
  • 8. NumPy Arrays
    • 8.1. Arrays Basics
    • 8.2. Array Computation
    • 8.3. NumPy Randomness
  • 9. Pandas
    • 9.1. Pandas Series
    • 9.2. Handling Datasets
    • 9.3. Pandas DataFrames
    • 9.4. Missing Data
    • 9.5. Combining Datasets
    • 9.6. Aggregation and groupby

Part IV. Data Visualization

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

Part V. Inferential Statistics

  • 14. Probability and Statistics
    • 14.1. Descriptive Statistics & Distribution
    • 14.2. Sampling Variability
  • 15. Testing Hypothesis
    • 15.1. Assessing Model #1
    • 15.2. Assessing Model #2
    • 15.3. Hypotheses and p-Value
  • 16. Comparing Two Samples
    • 16.2. A/B Testing
    • 16.3. Deflategate
    • 16.4. Causality
  • 17. Estimation
    • 17.1. Percentiles
    • 17.2. The Bootstrap
    • 17.3. Confidence Intervals
  • 18. Prediction with Regression
    • 18.1. Correlation
    • 18.2. The Regression Line
    • 18.3. The Method of Least Squares
    • 18.4. Visual Diagnostics

Part VI. Machine Learning

  • 19. Machine Learning
  • 20. Multiple Regression
    • 20.5. Linear Regression
    • 20.6. Multiple Regression
  • 21. Classification
    • 21.1. Nearest Neighbors
    • 21.2. Training and Testing
    • 21.3. Rows of Tables
    • 21.4. Implementing the Classifier
    • 21.5. The Accuracy of the Classifier
  • 22. Introduction to Clustering
    • 22.5. K Means Clustering with Python
    • 22.6. K Means Clustering with Python

Appendices

  • 23. On Programming
  • Repository
  • Open issue

Index

E | S | T

E

  • expression

S

  • statement

T

  • type casting

By Tsangyao Chen

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