Learn Python, R, machine learning, social media scraping, and much more from these free data science books you can download today.
What are the best books for learning data science?
First things first: if you want to learn to do data science, the most important thing you can do is get your hands on some real-world data and start coding. Our learning platform is designed to do that, getting you hands-on and writing real code from day one. Even if you're not using Dataquest, your primary approach to learning data skills should be hands-on.
But what can you do to keep learning in those moments when you’re not sitting in front of a computer? Read some data science books!
As a student we recently spoke with pointed out, ebooks are a great way to immerse yourself in data science at times when you can’t actually get hands-on with code — like on a bus ride, for example, or while waiting in line.
You can also listen to bools like podcasts if you use an ebook app with a “read aloud” feature, or decide to pay for an audiobook.
So what books should you read? Below, we've listed some of the best. And the even better news? Many of these books are totally free!
Note: Some of the links below are PDF links. We've tried to link to the free versions of books where possible.
(These are books that might help get you motivated to start or continue your data science journey, or help you better understand important issues in the data science field. You won't learn many practical skills from them, but they're good reads that help show how data and statistics are used in the real world).
Weapons of Math Destruction - One of the most popular nonfiction works about how "big data" and machine learning are not as unbiased as they might appear. Written by a former Wall Street quantitative analyst.
Big Data: A Revolution That Will Transform How We Live, Work, and Think - A good "big picture" read on how data and machine learning are changing lives in the real world — and on what else is likely to change in the future. If you've heard about the hype but aren't really sure how data science can affect things, this is a good place to start.
Naked Statistics: Stripping the Dread from Data - A good read on statistics and data for the layperson. If you're interested in learning data science but it's been a while since your first math course, this is a great book to help you build confidence and intuition about how statistics are useful in the real world.
Invisible Women: Data Bias in a World Designed for Men - Understanding how biases in our data can create inequalities in the real world is critical for anyone working with data to understand. This book details how aspects of gender inequality can be traced to data that treats men as the "default."
Numsense: Data Science for the Layman - A self-described "gentle" introduction to data science and algorithms, with minimal math. This is used as a textbook in some university courses, and it's a good place to start if you're interested in data but a little bit afraid of the math. (By the way, you don't have to be good at math to learn coding — in fact, it doesn't even really help).
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are - This book is essentially Freakonomics for data science. It's an interesting read that will also help you get an idea about how to approach answering different kinds of questions using data.
Algorithms of Oppression: How Search Engines Reinforce Racism - Another book on how algorithms contribute to inequality, this one focused on search engines. Algorithmic bias, and the ways it is created (and can be avoided) is really important for anyone who wants to work with data to understand.
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The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
The catalog of resources is endless, here are my recommendations. Resources for Learning Data Science