Elements of Data Science

by Allen B. Downey

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.

This clear, concise introduction to the data-science discipline is for people with no programming experience. Using Python, a beginner-friendly language popular within the industry, the book presents a basic yet powerful set of tools and methods that allow you to do real work in data science as quickly as possible—everything from answering questions and guiding decision-making under uncertainty, to creating effective data visualizations that have a real impact. Concepts are explained in simple terms, and exercises in each chapter demonstrate the practical purposes of various skill sets. Practical and hands-on, the author’s clever organization of content follows the steps of a data-science posing and refining questions, cleaning and validating data, exploratory analysis and identifying relationships between variables, generating predictions, and designing visualizations that tell a compelling story. Upon finishing the book, you’ll be able to execute your own data projects from start to finish!

Learn how

Genres: Technology

304 pages, Paperback

Expected publication January 2, 2079

About the author

Allen Downey is a Professor Emeritus at Olin College and the author of a series of freetextbooks related to software and data science, including Think Python, Think Bayes, and Think Complexity, which are also published by O’Reilly Media. His blog, Probably Overthinking It, features articles on Bayesian probability and statistics. He holds a Ph.D. in computer science from U.C. Berkeley, and M.S. and B.S. degrees from MIT.