If you want to read data science books without paying money then today I will tell you 5 data science books that you can read for free so let’s start
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1. Head First Statistics: A Brain-Friendly Guide
Head First Statistics brings this typically dry subject to life, teaching you everything you want and need to know about statistics through engaging, interactive, and thought-provoking material, full of puzzles, stories, quizzes, visual aids, and real-world examples
2. Practical Statistics For Data Scientists
Statistical methods are an important part of data science, but very few data scientists have formal training in statistics. Basic statistics courses and books rarely cover topics from a data science perspective. This practical guide explains how to apply various statistical methods in data science, tells you how to avoid abusing them, and offers advice on what is important and what isn’t.
Many data science sources involve statistical methods but lack a deeper statistical perspective. If you are familiar with the R programming language and statistics, this quick reference bridges the gap in an accessible and readable format.
3. Introduction to Probability, Second Edition
Developed from the acclaimed statistics course at Harvard, Introduction to Probability provides a foundational language and tools for understanding statistics, randomness, and uncertainty. This book covers a wide range of applications and examples ranging from coincidences and paradoxes to Google PageRank and Markov Chain Monte Carlo (MCMC). Other application areas researched are genetics, medicine, computer science, and information theory.
4. R For Data Science
R4DS teaches you how to do data science with R: you’ll learn how to bring your data into R, put it in the most useful structures, transform it, visualize it, and model it. This book provides training in data science skills. Just as a chemist learns to clean test tubes and fill a laboratory, you will learn how to clean data and draw graphs—and much more. These are the skills that make data science possible, and here are the best practices for doing all of those things with R. You’ll learn how to use graph grammar, sound programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discovery as you explore, visualize, and explore data.
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5. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data
Data Science and Big Data Analytics are all about harnessing the power of data to create new insights. This book covers a wide range of activities, methods, and tools used by data scientists. Content focuses on concepts, principles, and practical applications applicable to any industrial and technological environment, and learning is supported and explained through examples that you can repeat with open-source software.