Data Science in Practice

Data Science in Practice

Section 1: Understanding Data

1. Introduction to Data Science
2. Tabular Data
3. Querying and Describing Data
4. Understanding Assumptions and Data Cleaning
5. Aggregation and Extension of Data
6. Missing 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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Hypothesis Testing.

Hypothesis Testing.¶

See the Hypothesis Testing chapter in inferential thinking

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Exploratory Data Analysis (EDA): Part I

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Aggregation and Extension of Data

By Aaron Fraenkel
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