Principles and Techniques of Data Science

Frontmatter

To the Reader
Prerequisites
Notation

The Data Science Lifecycle

1. The Data Science Lifecycle
2. Generalizing from Data
3. Modeling and Estimation
4. [In progress] Case Study

Rectangular Data

5. Relational Databases and SQL
6. Data Tables in Python

Understanding The Data

7. Data Representation
8. [In Progress] Data Quality
9. [In Progress] Exploratory Data Analysis
10. Data Visualization
11. [In progress] Case Study: Berkeley Policing
- 11.1. Cleaning the Calls Dataset
- 11.2. Cleaning The Stops Dataset

Other Data Sources

12. Working with Text
13. Web Technologies

Linear Modeling

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

Multiple Linear Modeling

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

Classification

23. Classification

Replicability

24. [In progress] Replicable Research
- 24.1. P-hacking

Extra Topics

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

Appendices

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

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17. [In progress] Case Study: Donkey Dimensions¶

16.5. Fitting a Linear Model Using Gradient Descent 17.1. Linear Regression Case Study

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

License: CC BY-NC-ND 4.0