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

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
References

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[In progress] Replicable Research

24. [In progress] Replicable Research¶

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23.8. Multiclass Classification

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24.1. P-hacking

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

License: CC BY-NC-ND 4.0