10 Best Statistics Book for Data Science Books

Data science is a rapidly growing field that combines statistics, computer science, and domain knowledge to extract insights from data. With the rise of big data, the demand for data scientists has skyrocketed, making it a popular career choice for many.

Whether you’re just starting out in the field or looking to deepen your understanding, reading books is an excellent way to gain knowledge and stay up-to-date with the latest trends.

In this article, we’ll recommend the top books on data science that you should add to your reading list. From foundational works that provide an overview of the field to more advanced texts that delve into specific topics, these books will provide you with a comprehensive understanding of data science and its applications.

They cover a range of subjects, including statistics, machine learning, data visualization, and more. Whether you’re a seasoned data scientist or just starting out, these books are sure to enrich your understanding of this exciting and rapidly evolving field.

Recommended Data Science Books

Lots of sources for someone to learn new things. Nowadays, there are lots of videos or podcasts that discuss practical sciences comprehensively for us to know.

But back to the primary sources of knowledge, books are still one of the windows of world knowledge today. Here are 5 book recommendations for those of you who want to learn Data Science from scratch:

1. Python Crash Course

Author: Eric Mathes

This book, written by Eric Mathes, is suitable for friends who want to start learning Data Science without a programming background.

This book discusses packaged basic programming sciences so ordinary people can easily understand them. This book also provides exercises that you can do to hone your skills and make sure the reader understands what is being said.

2. R for Data Science


For those of you who want to start learning Data Science with the R programming language, this is your go-to! O’Reilly’s books have indeed become a source of learning technology skills worldwide.

R for Data Science introduces R, RStudio, and tidyverse, a collection of R packages designed to make learning data science fast and fun.

In it, you will understand the tools needed for your work and practice ways of thinking to align data with your goals. Don’t worry if you don’t have programming experience before this book was created for friends ready to learn R from scratch.

3. Naked Statistics: Stripping the Dread from the Data

Author:  Charles Wheelan

Wheelan, in this book, explains the basic concepts of statistics in a simple and fun way by inserting interesting and relatable real-world examples.

In addition to understanding the concept of statistics, you will also understand better why statistics are so loved and vital today. Reading this book helps you to be more critical of news or arguments that take statistics into account.

4. The Algorithm Design Manual

Author: Steven S. Skiena

This book by Steven S. Skiena is suitable for beginners because it presents the fundamentals of computer science, which explains algorithms and Data structures, and how algorithms shape the world today.

This book can help you take the big picture of an incident to analyze and break down the problem into a collection of information that can be processed into something of value.

5. Storytelling with Data: A Data Visualization Guide for Business Professionals


Apart from understanding how to obtain and process data, as a Data Scientist, you also need to know how to visualize data well. This book outlines the fundamentals of data visualization and how you can communicate effectively with data.

In addition to basic theory and concepts, many concrete examples can be put into practice immediately to make it easier for readers to understand.

6. Think Stats

Author:  Allen B. Downey

Think Stats is a comprehensive guide for data science beginners with prior knowledge in Python programming.

This book provides in-depth insights into exploratory data analysis, distributions, and functions. It delves into more complex topics like hypothesis testing, regression, and time series analysis.

This book is considered one of the best resources for statistics in data science, but it’s important to have a solid grasp of Python before diving in.

The book includes many code examples written in Python, making it easier to understand and apply the concepts learned. In conclusion, Think Stats is a must-read for anyone looking to gain a strong foundation in statistics for data science.

7. The Signal and The Noise

Author: Nate Silver

The Signal and the Noise is an exceptional book that offers insights into the practical application of statistics and probability in data science.

Written by Nate Silver, a renowned statistician and forecaster, the book reached the New York Times Best Seller list within a week of its publication.

In the book, Silver shares his wealth of knowledge and experience, drawing from real-life examples and his own successful predictions in various fields to illustrate the art of mathematical modeling in statistics and probability.

He highlights the importance of distinguishing between true signals and noisy data, and provides guidance on how to avoid common mistakes.

If you’re looking to enhance your understanding of statistics for data science, The Signal and the Noise is an excellent choice.

The author’s unique perspective and practical approach make it a valuable resource, providing a different perspective compared to attending a bootcamp or online course in data science.

8. Statistics in Plain English

Author: Timothy C. Urdan

“Statistics in Plain English” is a comprehensive guide to the fundamentals of statistics, designed to make the subject accessible to everyone.

The book uses simple language to explain complex statistical concepts, with each chapter focusing on a different statistical technique.

From the basics of central tendency and distributions to advanced topics like T-tests, regression, and ANOVA, this book provides a thorough overview of statistics and includes examples and links to helpful resources.

Whether you’re a beginner or looking to brush up on your skills, “Statistics in Plain English” is an excellent choice for anyone seeking a comprehensive guide to statistics in data science.

9. Practical Statistics for Data Science

Author: Peter Bruce and Andrew Bruce

“Practical Statistics for Data Scientists: An Essential Guide to Statistical Methods in Data Science” is an ideal title for a book that provides a practical approach to statistics for data science.

Written by Peter and Andrew, this book offers a comprehensive guide to the various statistical methods used in data science, while also highlighting common mistakes to avoid.

Starting with the basics of exploratory data analysis, the authors cover topics such as random sampling, experimental design, regression, classification techniques, and machine learning methods.

With a focus on practical applications, this book provides a thorough understanding of the statistical perspective that is essential for the role of a data scientist.

If you have a background in R programming, “Practical Statistics for Data Scientists” is the perfect book for enhancing your skills in statistics for data science.

10. Computer Age Statistical Inference

By Bradley Efron and Trevor Hastie

Computer Age Statistical Inference is a unique book that explores the evolution of statistical inference through the ages. From pre-computer era to modern-day, this book takes you on a journey through the advancements in the field of statistics.

Divided into three parts – Classic Statistical Inference, Early Computer-Age Methods, and Twenty-First-Century Topics – this book provides a comprehensive overview of the development of statistical analysis.

It’s not just a statistics textbook for data science, but also a great read that contrasts the algorithmic and inferential aspects of statistical analysis.

With its fascinating insights into the history of statistics, Computer Age Statistical Inference is a must-read for anyone interested in the field of data science.

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