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Elements of Data Science

by Allen B. Downey
Winter 2025, 304 pp.
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Elements of Data Science is an introduction to the discipline for people with no programming experience. Concepts are explained clearly and concisely, and exercises in each chapter demonstrate the practical purposes of various skill sets. The organization of the book itself follows the steps of a data science project: posing and refining questions, cleaning and validating data, exploratory analysis and identifying relationships between variables, generating predictions, and designing data visualizations that tell a compelling story.

Author Bio 

Allen Downey is a Staff Producer at Brilliant and Professor Emeritus at Olin College, where he taught Modeling and Simulation and other classes related to software and data science. He is the author of several textbooks, including Think PythonThink Bayes, and Elements of Data Science. Previously, he taught at Wellesley College and Colby College. He received his Ph.D. in computer science from the University of California, Berkeley in 1997. His undergraduate and master's degrees are from the Civil Engineering department at MIT. He is the author of Probably Overthinking It, a blog about data science and Bayesian statistics.

Table of contents 

Part I: From Python to Pandas
1. Variables and Values
2. Times and Places
3. Lists and Arrays
4. Loops and Files
5. Dictionaries
6. Plotting
7. Dataframes and Series
Part II: Exploratory Data Analysis
8. Distributions
9. Relationships

10. Regression
Part III: Statistical Inference
11. Resampling
12. Bootstrap Sampling
13. Hypothesis Testing
Part IV: Case Study: Political Alignment
14. Political Views
15. Political Alignment and Outlook
Part V: Case Study: Algorithmic Fairness
16. Classification
17. Calibration

The chapters in red are included in this Early Access PDF.