# 5 Free Books to Master Statistics for Data Science

Statistics is a must-have skill for data science. And here are 5 free books that’ll help you learn all the statistics you need as a data professional.

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To learn data science, you also need a solid foundation in math. And statistics is one of those essential math skills for data science.

However, learning statistics can be intimidating especially if you’re from a specialization that isn’t math or computer science. To help you get started, we’ve compiled a list of free books that make statistics for data science accessible.

Most of these books take a hands-on approach to statistics concepts, which is what you need to use statistics effectively as a data scientist. So let’s go over these stats books.

# 1. Introductory Statistics

The Introductory Statistics book is an accessible intro to statistics that covers what a semester-long introductory statistics course in colleges typically covers.

Available for free access on OpenStax and written by a team of contributing expert authors, this book takes an application-first approach to statistics rather than a theory-first approach and includes examples in exercises for each topic.

This book will help you learn the following:

- Sampling and data
- Descriptive statistics
- Topics in Probability and random variables
- Normal distribution
- The Central Limit theorem
- Confidence intervals
- Hypothesis testing
- The Chi-Square distribution
- Linear regression and correlation
- F distribution and one-way ANOVA

Link: Introductory Statistics 2e

# 2. Introduction to Modern Statistics

Introduction to Modern Statistics is a free online textbook from the OpenIntro project and is written by authors Mine Çetinkaya-Rundel and Johanna Hardin.

If you want to learn statistics foundations for effective data analysis, then this book is for you. The contents of this book are as follows:

- Introduction to data
- Exploratory data analysis
- Regression modeling
- Foundations of inference
- Statistical inference
- Inferential modeling

Link: Introduction to Modern Statistics

# 3. Think Stats

Think Stats by Allen B. Downey will help you learn and practice statistics concepts using Python.

So you can apply your Python skills to learn statistics and probability concepts for working with data effectively. As you work through the book, you’ll get to write short Python programs and practice with real datasets to reinforce your understanding of statistics concepts.

The topics covered are as follows:

- Exploratory data analysis
- Distribution
- Probability mass functions
- Cumulative distribution functions
- Modeling distributions
- Probability density functions
- Relationships between variables
- Estimation
- Hypothesis testing
- Linear least squares
- Regression
- Survival analysis
- Analytic methods

Link: Think Stats 2e

# 4. Computational and Inferential Thinking

Computational and Inferential Thinking: The Foundations of Data Science by Ani Adhikari, John DeNero, and David Wagner will help you learn statistics foundations for data science.

This book was developed as a companion to the Data 8: Foundations of Data Science course offered at UC Berkeley. The topics covered in this book include:

- Introduction to data science
- Programming in Python
- Data types, Sequences, and Tables
- Visualization
- Functions and Tables
- Randomness
- Sampling and empirical distribution
- Hypothesis testing
- Estimation
- Regression
- Classification

Link: Computational and Inferential Thinking: The Foundations of Data Science

# 5. Probabilistic Programming and Bayesian Methods for Hackers

Probabilistic Programming and Bayesian Methods for Hackers or Bayesian Methods for Hackers is a popular book on Bayesian methods in statistics.

"Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;) - Source

You’ll become familiar with probability theory and Bayesian inference all while using the PyMC package. The contents of this book are as follows:

- Introduction to Bayesian methods
- The PyMC library
- Markov Chain Monte Carlo
- The Law of Large Numbers
- Loss functions
- Priors

Link: Probabilistic Programming and Bayesian Methods for Hackers

# Wrapping Up

I hope you found this round-up of free statistics books helpful. The mix of theory and hands-on practice should help you level up your data science skills and make more informed decisions when working with large real-world datasets.

If you prefer working through free courses or looking to supplement your reading with courses, check out 5 Free Courses to Master Statistics for Data Science.

** Bala Priya C** is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she's working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.