Principles and Techniques of Data Science

Frontmatter

To the Reader
Prerequisites
Notation

The Data Science Lifecycle

1. The Data Science Lifecycle
2. Questions and Data Scope
3. Simulation and Data Design
4. Modeling and Summary Statistics
5. [In progress] Case Study: Why is my Bus Always Late?

Rectangular Data

6. Working With Dataframes Using pandas
7. Working With Relations Using SQL

Understanding The Data

8. Wrangling Files
9. Wrangling Dataframes
10. Exploratory Data Analysis
11. Data Visualization
12. Case Study: Data Science for Accurate and Timely Air Quality Measurements

Other Data Sources

13. Working with Text
14. Web Technologies

Linear Modeling

15. Linear Models
16. Theory for Inference and Prediction
17. Gradient Descent and Numerical Optimization
18. Case Study: How to Weigh a Donkey

Multiple Linear Modeling

19. [In progress] Multiple Linear Regression
- 19.1. Multiple Linear Regression
- 19.2. Inference for Multiple Linear Regression
20. Feature Engineering
- 20.1. The Walmart dataset
- 20.2. Predicting Ice Cream Ratings
21. The Bias-Variance Tradeoff
22. Regularization
23. [In progress] Case Study: Multiple Linear Models

Classification

24. Classification

Replicability

25. [In progress] Replicable Research
- 25.1. P-hacking

Extra Topics

26. Dimensionality Reduction and PCA
27. [In progress] Decision Trees and Random Forests
28. [In progress] Clustering

Appendices

Probability Review
Vector Space Review
Statistical Inference Review
- Hypothesis Testing and Confidence Intervals
- Permutation Test
Reference Tables
Contributors
References

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[In progress] Replicable Research

25. [In progress] Replicable Research¶

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24.8. Multiclass Classification

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25.1. P-hacking

By Sam Lau, Joey Gonzalez, and Deb Nolan
© Copyright 2022.

License: CC BY-NC-ND 4.0