The Best Data Science Books

If you’re looking to get into data science, or if you’re already a practitioner and want to brush up on your skills, one of the best things you can do is read some of the best data science books. Luckily, there are plenty of great options out there, covering everything from the basics of data analysis to more advanced topics like machine learning.

To help you find the right book for you, we’ve put together a list of some of the best data science books available right now. Whether you’re just getting started or you’re a seasoned pro, there’s something here for everyone. So without further ado, here are the best data science books to read in 2020.

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python 2nd Edition

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

Data Science from Scratch: First Principles with Python 2nd Edition

Data Science from Scratch is another great option for those just getting started in data science. In this book, Joel Grus takes readers through all the basics of data science using Python, starting with how to load and manipulate data and moving on to more advanced topics like exploratory analysis and modeling. Even if you don’t have any prior experience with Python, Grus does a great job of explaining everything clearly so that you can follow along without any problems.

Introduction to Machine Learning with Python by Andreas Muller and Sarah Guido

Machine learning is one of the hottest topics in data science right now, so it’s definitely worth reading up on if you’re interested in staying ahead of the curve. In Introduction to Machine Learning with Python, Muller and Guido provide a detailed yet accessible introduction to machine learning using Python. They cover all the essential concepts, including supervised and unsupervised learning, feature engineering, and model evaluation and tuning. If you want to learn about machine learning but don’t know where to start, this is an excellent book to check out.

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupiter in the process.

Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.

Python Data Science Handbook: Essential Tools for Working with Data

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

These are just a few of the many great data science books that are available right now. No matter what your level of experience is, there’s sure to be a book on this list that will help you improve your skills and knowledge. So what are you waiting for? Pick up one (or more!) of these books today and start learnings!