Data Science in Practice

Section 1: Understanding Data

Section 2: Collecting Data

7. Data Collection
8. Information Extaction
- 8.1. Text Processing
- 8.2. Natural Language Processing
9. Introduction to Features
- 9.1. Feature Engineering
- 9.2. Data Pipelines

Section 3: Modeling With Data

10. Modeling Basics
- 10.1. Introduction to Statistical Models
- 10.2. Building Modeling Pipelines
11. Bias and Variance
12. Evaluating Models; Fairness

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Evaluating a Model

See Lecture Notes

By Aaron Fraenkel
© Copyright 2021.