Principles and Techniques of Data Science

Frontmatter

To the Reader
Prerequisites
Notation

The Data Science Lifecycle

1. The Data Science Lifecycle
2. Data Scope
3. Theory for Data Design
4. Modeling and Estimation
5. [In progress] Case Study

Rectangular Data

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

Understanding The Data

8. Data Representation
9. [In Progress] Data Quality
10. [In Progress] Exploratory Data Analysis
11. Data Visualization
12. Case Study: Correcting PurpleAir Measurements

Other Data Sources

13. Working with Text
14. Web Technologies

Linear Modeling

15. Linear Models
16. Probability and Generalization
17. Gradient Descent and Numerical Optimization
18. [In progress] Case Study: Donkey Dimensions
- 18.1. Linear Regression Case Study

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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Probability Review

By Sam Lau, Joey Gonzalez, and Deb Nolan
© Copyright 2021.

License: CC BY-NC-ND 4.0