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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Contents

Content Summary

Modeling Basics

Contents

Content Summary

Modeling Basics¶

Content Summary¶

Definitions: Statistical inference and prediction
Developing modeling pipelines

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Features with Data Pipelines

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Introduction to Modeling

By Aaron Fraenkel
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