Today, we’ll be discussing the 10 best statistics books for data science beginners as statistics is a fundamental skill that every Data Scientist should have.

Do you wanna know why?

First, let’s define statistics in formal terms.

“Statistics is the science of collecting and analyzing numerical data in large quantities, to infer proportions in a whole from those in a representative sample”.

Now, just imagine you have an unclean dataset. Will you directly feed it to the Machine Learning model? No, right!!

You first need to understand your data clearly and also make it clean. Like you need to impute the missing values with the mean, median, mode, etc. Also, you need to detect the outliers. Here your statistics knowledge comes to the rescue. That is why statistics is imperative and a very crucial part of Data Science.

“If Statistics is boring, you have got the wrong numbers.”

Edward Tufte

## Why books for statistics?

## So let’s deep dive into the best statistics books for Data Science

### 1. Practical Statistics for Data Scientists

#### Authors: **Peter Bruce & Andrew Bruce**

#### Best for: **Beginners**

#### Why to read this book?

If you are only ever going to buy one statistics book, or if you are thinking of updating your library, this book would be an excellent choice.

Not every statistical concept is required for data science. This book covers each and every concept associated with data science in a very elucidated manner.

Not just that, the book is completely practical focussed. The book along with an explanation also contains code written in R. So, if you are the one who is looking for implementing the code as well, this book is for you.

The book will enable you to gain proficiency in the basics of statistics for Data Science.

#### Major Topics Covered

- Descriptive statistics
- Probability
- Randomization
- Sampling
- Types of distributions

#### Programming Language used: **R**

### 2. An Introduction to Statistical Learning with Applications in R

#### Authors: **Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani**

#### Best for: **Beginners**

#### Why to read this book?

Precisely what it sounds like! This book is an absolute goldmine when it comes to the way of explanation. This book provides a solid introduction to Machine Learning Algorithms based on statistical principles. It walks through various forms of statistical learning, regression, classification, SVM, tree-based methods along with code written in R. The book puts more emphasis on the practical applications of the concepts.

This book is suitable for individuals in quantitative fields who wish to use statistical tools to analyze the data.

Not just algorithms, it also includes statistical techniques behind resampling techniques like cross-validation.

This book is completely practical focussed. You can practice along with understanding the concepts.

#### Programming Language used: **R**

### 3. Think Stats

#### Authors: **Allen B. Downey**

#### Best for: **Beginners** **with a basic introduction to Python**

#### Why to read this book?

One of the most important characteristics of a Data Analyst is to be able to efficiently inspect data, analyze it, and extract useful information from it. This book does a very good job of introducing statistical concepts through practical examples of data science. The book will teach you to perform statistical analysis computationally and apply descriptive statistics in Python. The book uses a case study to explain the concepts through real-life examples.

The uniqueness of the book is that it doesn’t rely on any modules or libraries for code. It has codes written from scratch like Mean Squared Error, etc.

This book is about turning knowledge into data

Allen B. Downey

#### Major Topics Covered

- Descriptive Statistics
- Different types of distribution functions
- Operations performed on Distribution functions
- Probability
- Hypothesis Testing
- Estimation
- Correlation

#### Programming Language used: **Python**

### 4. The Cartoon Guide to Statistics

#### Authors: **Larry** **Gonick & Woollcott Smith**

#### Best for: **Beginners**

#### Why to read this book?

This is the most comprehensive book of the bunch. It uses a lot of symbols to explain the concepts which is rather a fun way of learning. It lays out the basics of the field in a lucid manner and also covers advanced concepts like the confidence interval, regression analysis.

The book does a great job of a gradual learning curve mixed with an emphasis on real-world applications. It is an informal yet informative book that presents its contents as graphically and intuitively as possible.

#### Major Topics Covered

- Probability
- Sampling
- Hypothesis Testing
- Experimental Design
- Regression

### 5.Bayesian Methods for Hackers

#### Authors: **Cameron Davidson-Pilon**

#### Best for: **Beginners** **with hands-on experience with Python**

#### Why to read this book?

First of all, what is Bayesian Inference? It is a branch of statistics that deals with understanding uncertainty. As a data scientist, you need to understand the uncertainty around the predictions that your model is delivering. The book gives deep insights into Bayesian analysis. The book is pragmatic in nature.

The best part of the book is that the chapters are in Jupyter Notebook form so you can run and tinker with the code as well.

#### Major Topics Covered

- Bayesian Methods
- Python’s PyMC library
- Markov Chain Monte Carlo
- Loss Functions
- Model Validation

#### Programming Language used: **Python**

### 6. Naked Statistics: Stripping the dread from the data

#### Authors: **Charles Wheelan**

#### Best for: **Beginners**

#### Reasons to read this book

“Statistics is like a high-caliber weapon: helpful when used correctly”.

The author has tried to explain each and every concept very intuitively and includes witty and relatable examples. The book will chuckle at you all the time with funny proactive real-life examples. It also discusses how statistics can be used and misused. Wheelan himself says in the book

Overall, the book explains the concepts from basics to advanced in a crystal clear manner.

#### Major Topics Covered

- Descriptive Statistics
- Central Limit Theorem
- Inference
- Basic Probability
- Program Evaluation

### 7. Computer Age Statistical Inference

#### Authors: **Bradley Efron & Trevor Hastie**

#### Best for: **Intermediates having prior basic statistical knowledge**

#### Why to read this book?

The book describes the nature of statistics by displaying multiple examples of the way the field has evolved over the years. This book is full of equations, derivations, and theorems. The explanations are vivid and easy to understand. The book will make you look at the concepts from a different angle.

The book covers the theory as well as mathematics behind most of the machine learning algorithms.

#### Major Topics Covered

- Inferential Statistics
- Bayesian Inference
- Frequentist Inference
- Fisherian Inference
- Machine Learning
- Large Scale Hypothesis Testing

### 8. Statistics in Plain English

#### Authors: **Timothy C. Urdan**

#### Best for: **Beginners**

#### Why to read this book?

The beauty of this book is the simplicity of writing. The book presents brief explanations of statistical concepts and techniques in a simple, everyday language. It helps the reader gain a better understanding of how statistics work and how to interpret them correctly. The book is filled with some excellent examples.

This book particularly has a strong focus on the Gaussian distribution and hypothesis testing. It is an introductory book and does not cover many advanced concepts.

#### Major Topics Covered

- Measures of Central Tendency
- Standardization and Normalization
- Hypothesis Testing

### 9. Statistics Done Wrong: The Woefully Complete Guide

#### Authors: **Alex Reinhart**

#### Best for: **Intermediates and Advanced**

#### Reasons to read this book

It is an essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. It discusses many common errors in statistical analysis. Knowing the probable errors that can occur can help you understand the concepts better. The book also discusses the solutions to avoid fallacies or any pitfalls.

This book is particularly for those who have prior research experience in statistics. You never want your research to get wrong due to wrong statistics. This is where the author does a commendable job of explaining the nitty-gritty of the statistical errors and ways to resolve them.

#### Major Topics Covered

- p-value and base rate fallacy
- Bad judges of significance
- Continuity errors

### 10. Head First Statistics: A Brain-Friendly Guide

#### Authors: **Dawn Griffiths**

#### Best for: **Beginners**

#### Why to read this book?

The author succeeds in bringing a subject to life. It teaches you everything about statistics through engaging, interactive, and thought-provoking material and real-life examples. It is an outstanding introduction for those who need a good guide that explains what statistical operations are. The examples used are simple, yet intuitive. The book is structured in an excellent way that builds progressively on previous concepts.

It assumes little to no prior experience in statistics. It is highly recommended for absolute beginners.

#### Major Topics Covered

- Descriptive Statistics
- Probability
- Distributions
- Sampling
- Hypothesis testing

## Conclusion

So, here we mentioned the best books to learn statistics. You can refer to these books to master statistics and probability and delve deep into the field of Data Science.

If you want to understand statistics with practical examples in layman terms, you can join our upcoming Bootcamp **“Decode Statistics with real-world examples”** organized by Let the Data Confess Pvt. Ltd. It is ideal for those who want to start or switch their career in data science.