As data scientists, our work not just revolving around creating machine learning models or cleaning data. It is also involved in analyzing data and proves our intended hypotheses.

I realized that many people who want to learn about data scientists not precisely fond of statistics, even though it was necessary, in my opinion. For that reason, I want to show you my top five free statistic books you could get from the web.

# 1. Probability and Statistics Cookbook

Written by **Matthias Vallentin.**

The book: statistics.zone

This book already summarized the content with the paragraph, “The probability and statistics cookbook is a concise representation of various topics in probability theory and statistics. It provides a comprehensive mathematical reference reduced to its essence, rather than aiming for elaborate explanations.”

For a starter, I think this book would not excite you as much as the other book I would show you later. Why?

Because this book could be considered as a cheat book for various topics in probability theory and statistics.

They did not provide any in-depth explanation in this book. Instead, they only contain the formula and the probabilistic theory graph.

To me, this is a great cheat book because I already understand many of the terms, but for you who did not feel familiar with the statistic, just keep the book first and learn more about the field through another book.

# 2. Think Stats 2E

Written by **Allen B. Downey**.

the book: greenteapress.com

Think Stats by Downey is an excellent book for you who like to practice the theory rather than just read the complicated explanation.

This book covers many basic statistic concepts, such as descriptive statistics, data exploratory, and the hypothesis. The book aims more to a beginner in statistics but also a beginner in Python language. That is why this book is perfect for the beginner in the data science field.

While this book is excellent for a beginner, you might want more explanation of some concepts from other resources.

My suggestion is to read this book while reading another resource at the same time for a more in-depth explanation but made this book as your first reference book for the statistic coding parts.

# 3. Introduction to probability

Written by **Charles M. Grinstead & J. Laurie Snell.**

The link below is for the hardcover version, which is not free; for the free soft copy version, visit here.

According to the summary, this book is designed for an introductory probability course at the university level for sophomores, juniors, and seniors in mathematics, physical and social sciences, engineering, and computer science.

As a Data Scientist, we would often work with the probability and likelihood concept. For that reason, we need to understand more about the probability concept.

This book is written nicely for a beginner to understand the intricate detail of statistical probability. Although some of the terms are still hard to know without a significant focus.

Nevertheless, I recommend this book for the beginner in the statistic or even someone who just needs a refresher.

# 4. Bayesian Methods for Hackers

Written by **Cameron Davidson-Pilon.**

the book: camdavidsonpilon.github.io

This is a fantastic book for you who don’t like math but want to understand the Bayesian Concept.

Cameron Davidson-Pilon, in this book, is trying to introduce Bayesian from a computational perspective to bridging theory and practice by using computational power.

I love the way this book is written; by breaking down little by little the concept but still use the language that is understood by people who do not come from the statistic field.

While this book is easy to follow, I am still suggesting for you to learn about what is the Bayesian concept before reading this book.

But, I still can’t recommend you enough about this book.

# 5. A First Course in Design and Analysis of Experiments

Written by **Gary W. Oehlert.**

For the free soft copy version, visit here.

For the most part, data scientists need to design their experiments and capable of testing their hypotheses. You need to prove what you find, after all.

The book summary gives us that this book is suitable for beginner or even statistic major. Unlike most texts for experimental design, this book offers a superb balance of both analysis and design.

Many technical terms were used in this book, but the way it is written is easy for any beginner to follow.

You might want to read this book while you try to work on some data project, so you could have a feeling for designing a data experiment and hypothesis proving.

# Conclusion

There is five free statistic book I recommend to everybody, they are:

**Probability and Statistics Cookbook**wrote by**Matthias Vallentin****Think Stats 2E**wrote by**Allen B. Downey****Introduction to probability**wrote by**Charles M. Grinstead & J. Laurie Snell****Bayesian Methods for Hackers**wrote by**Cameron Davidson-Pilon****A First Course in Design and Analysis of Experiments**wrote by**Gary W. Oehlert**

I hope it helps!