Data scientists rely heavily on their mastery of statistics. Data science is the mathematical subfield that facilitates the process of gathering, describing, analyzing, and drawing conclusions from information. Data scientists use statistics for various purposes, including but not limited to data analysis, experiment design, and statistical modelling.

Let's take a look at the best statistics books for data scientists.

An Introduction To Statistical Learning - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani 

A practical statistical introduction is in "An Introduction to Statistical Learning," which also teaches some of the most crucial modelling techniques, along with examples and applications. In addition, regression, classification, resampling techniques, tree-based methods, support vector machines, clustering, and other topics are among those covered in this book. R programming is used in the book to make it easier to apply statistical ideas practically.

Furthermore, this book teaches you how to analyze data using advanced statistical learning techniques, whether you're a statistician or not. Thus, one of the best statistics books for data science is An Introduction to Statistical Learning.

Computer Age Statistical Inference - Bradley Efron and Trevor Hastie

Computer Age Statistical Inference book discusses the theoretical underpinnings of the most prevalent machine learning algorithms for data scientists today. In addition, it provides an exhaustive overview of the Bayesian and Frequentist approaches to statistical inference.

Furthermore, complex concepts are through examples, such as classifying spam data, which accompany each explanation. This book best suits readers familiar with fundamental statistical concepts and data analysis notation.

Head First Statistics - Dawn Griffiths 

Head First Statistics is an excellent book on probability and statistics for data scientists. It teaches statistics using interactive and engaging content. It's jam-packed with stories, puzzles, visual aids, quizzes, and real-life examples.

This book will help you grasp statistics in such a way that you will be able to understand and apply the underlying vital points. It is also for college students learning statistics because of its friendly and easy-to-understand content.

How to Lie with Statistics - Darrell Huff

This book, "How to Lie with Statistics", is excellent for reviewing your fundamentals. It resembles a small set enriched with a wealth of information. The author makes concepts like correlation, regression, and inference clear. He describes how we can use statistical graphs to determine reality. Although the book is quite old, the ideas still hold today. It is the book that students have relied on for generations like a trusted friend.

Naked Statistics: Stripping the Dread from the Data - Charles Wheelan

The advanced statistics text Naked Statistics "brings statistics to life." The book begins with fundamental concepts such as normal distribution before moving on to more complex subjects. In addition, the book takes a small step away from technical details and focuses on the fundamental concepts of statistical analysis, providing numerous examples and case studies. It includes topics such as inference, correlation, and regression, as well as practical examples.

Practical Statistics for Data Scientists - Peter Bruce and Andrew Bruce

The book Practical Statistics for Data Scientists does a fantastic job of focusing solely on topics related to data science. So this book is unquestionably the one to pick if you're looking for something that will quickly give you just the knowledge you need to practice data science.

It gives clear definitions of all statistical terms, is chock full of numerous practical coded examples (written in R), and includes links to additional reading materials.

Statistics in Plain English - Timothy C. Urdan

This book, "Statistics in plain English", is not limited to the statistical methods employed by data scientists and computer programmers; it covers a vast array of topics in this field. However, it is written straightforwardly and explains complex statistical concepts anyone can understand.

Furthermore, the book was for students enrolled in a non-mathematics course, such as social science, that required familiarity with statistical concepts. Therefore, it provides sufficient theoretical coverage to comprehend the methods without requiring prior mathematical knowledge. This book is excellent for those without a math background who wish to enter the field of data science.

Think Stats - Allen B. Downey 

Think Stats is an excellent book for newcomers familiar with Python programming. The book begins by thoroughly describing the different concepts of exploratory data analysis. Following that, it discusses statistics distributions and distribution functions. Finally, it covers more complex subjects like time series analysis, regression, and hypothesis testing.

Additionally, Thinks Stats is unquestionably one of the best statistics books for people new to data science and will help you gain a solid understanding of the fundamental statistics used in data science. But before choosing this book as your first statistics and data science book, make sure you have a firm grasp of Python programming. It contains a lot of Python code examples.