Best Data Science Books
Looking for good data science books? On this page you’ll find a curated list of the best data science books of all time. Enjoy!
The 10 Best Data Science Books
1. An Introduction to Statistical Learning: With Applications in R by Gareth James, Trevor Hastie, Robert Tibshirani, Daniela Witten
Synopsis: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
2. Data Science for Business: What you need to know about data mining and data-analytic thinking by Foster Provost, Tom Fawcett
Synopsis: Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.
3. The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Friedman
Synopsis: This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics.
4. The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t by Nate Silver
Synopsis: With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.
5. Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman
Synopsis: You get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You’ll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.
6. Python for Data Analysis by Wes McKinney
Synopsis: Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications.
7. Doing Data Science by Rachel Schutt, Cathy O’Neil
Synopsis: Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.
8. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil
Synopsis: Tracing the arc of a person’s life, from college to retirement, O’Neil exposes the black box models that shape our future, both as individuals and as a society. Models that score teachers and students, sort resumes, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health—all have pernicious feedback loops.
9. Data Science from Scratch: First Principles with Python by Joel Grus
Synopsis: Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
10. Naked Statistics: Stripping the Dread from the Data by Charles Wheelan
Synopsis: As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more. For those who slept through Stats 101, this book is a lifesaver.