Elements of Data Science

Through practical projects and interesting exercises, learn how to work with data using Python—no prior programming knowledge needed!

Analyze vaccine efficacy, support for gun control, and more in this exercise-filled, beginner-friendly introduction to data science and Python, the leading programming language in the data science industry.

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.

Allen Downey is a Staff Scientist at DrivenData 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 Python, Think 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.