If you want to get into data science with a limited budget, this reading list is for you — it’s all about data science and related books that you can get for free!
Allen B. Downey’s free Python and math books
Allen B. Downey is a believer in free books, and has a whole article explaining why. Here are its concluding paragraphs:
A free book is the root of a tree of potential adaptations, translations, and entirely new books that branch out from the original. Free books transform readers into proof-readers, editors, anthologists, correspondents, contributors, collaborators, writers and authors.
If you are thinking about writing a book, start soon, release early and often, give up control but do a little policing, keep a contributor list, and make it free.
He’s written a number of free books, and the ones most applicable to data science are:
- Think Python, 2nd edition. A great introduction to programming and Python 3 that’s suitable for beginners, but also a great reference for people like me who have to bounce between languages. You can read it online or download it in PDF form, and code examples and solutions are available in this Github repo.
- Think Stats, 2nd edition. If you know Python but don’t know probability and statistics, this is your book! It encourages you to work with real datasets, and presents you with a case study using real data from the U.S. National Institutes of Health. You can read it online, or download it in PDF form, and the code examples and solutions are available in this Github repo.
- Think Bayes. Downey writes that most books on Bayesian stats (statistics with probabilities applied to it, or updating your thinking when presented with new evidence or data) express their ideas in complex math notation or use calculus. This book expresses its ideas using Python and simpler discrete math approximations: “what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops.” You can read it online, download it in PDF form, and the code examples are available in this Github repo and in this Jupyter notebook.
Bayesian Methods for Hackers
Bayesian Methods for Hackers is described as “an intro to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view”, and its key chapters are available online, for free, in Jupyter notebook form. The method for reading it that the authors recommend is to clone the book’s Jupyter notebook repo and run it on your local machine.
The Python Data Science Handbook
Another Python/data science book in Jupyter notebook form! This one assumes that you’re familiar with Python, as it’s all about the libraries that are most used for data science and machine learning: NumPy, Pandas, Matplotlib, and Scikit-Learn.
R Programming for Data Science
From the book site:This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science.
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This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.
This book is available for free in PDF, EPUB, and MOBI formats (there’s a $20 suggested price, but you can pay what you want).
R for Data Science
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.
The Art of Data Science
From the book site:This book writes down the process of data analysis with a minimum of technical detail. What we describe is not a specific “formula” for data analysis, but rather is a general process that can be applied in a variety of situations. Through our extensive experience both managing data analysts and conducting our own data analyses, we have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of our experience in a format that is applicable to both practitioners and managers in data science.
This book is available for free in PDF, EPUB, and MOBI formats (there’s a $20 suggested price, but you can pay what you want).