The problem companies face today is not a lack of data; On the contrary, it is the huge amounts of data that data scientists find difficult to deal with. Big data has revolutionized the data science industry as we know it, including the topics that data scientists deal with. While statistics have not historically been popular with data scientists, they play a huge fundamental role in better data analysis, prediction, and inference. It helps to sift through the data and present the results in a simple way, identifying hidden patterns and aspects of data, which plays a crucial role in data-driven decisions.

However, data scientists typically lack the in-depth knowledge of statistics that could fuel their knowledge generation. Furthermore, given the broad nature of statistics, not everything is relevant to data science. Considering this barrier, Analytics India Magazine has identified the best statistics books for data science.

**The signal and the noise: Why most predictions fail but some don’t**

### by Nate Silver

Designated “One of the Most Important Books of the Decade” by the New York Times Book Review, The Signal and the Noise is a comprehensive guide to making better predictions using statistical models. The book aims to prepare data scientists to communicate their findings clearly and concisely. Nate Silver is a popular blogger known for his baseball performance prediction system and 2008 election prediction, among other things. Drawing on his insights, this book guides data scientists through excerpts from some of the most successful forecasters in various fields and their real-life experiences in distinguishing “real signals” from noisy data, prediction errors to avoid, the prediction paradox, and more.

find the book here.

**Think about statistics**

### by Allen B Downey

Think Stats introduces probabilities and statistics for Python programmers and mainly covers concepts directly related to data science. With Python code examples, Think Stats is aimed at programmers with experience, teaching them statistical concepts through practical data analysis examples and encouraging them to work on real datasets. It is based on Bayesian methods and covers topics such as statistical reasoning, correlation, hypothesis testing, regression, time series analysis, survival analysis, distributions and methods of analysis. Downey’s other book, Think Bayes, examines solving statistical problems with Python code.

find the book here.

**Naked statistics: Take the fear out of the data**

### By Charles Wheelan

An advanced statistics book, Naked Statistics, aims to “bring statistics to life”. The book starts with basic concepts like normal distribution and then moves on to more complex topics. Filled with examples and case studies, the book takes a step away from technical detail and focuses on the underlying concepts of statistical analysis. It covers topics such as inference, correlation, regression, and practical examples.

find the book here.

**Statistics in plain English**

### by Timothy C Urdan

Statistics in Plain English covers common statistical techniques and concepts in an easy to understand way. Various chapters in the book explain and illustrate by example a statistical technique, including central tendency and description of distributions, *t tests*, regression, repeated measures, ANOVA and factor analysis. While not aimed at data scientists, the book is an ideal book for data science beginners and covers the topics of regression, distribution, factor analysis, and probability.

find the book here.

**Statistical inference of the computer age**

### by Bradley Efron and Trevor Hastie

Computer Age Statistical Inference explores data analysis and the data science revolution through classic inference theories of Bayesian, Frequentist, and Fischer theories. It talks about the theories behind machine learning algorithms with detailed explanations and application examples on topics like spam data. Topics covered in the book include Machine Learning, Deep Learning, Hypothesis Testing, Random Forests, Survival Analysis, Logistic Regression, Empirical Bayes, Jackknife and Bootstrap, Markov Chain Monte Carlo, and Inference by Model Selection. In the end, the book speculates on the future direction of data science and statistics.

find the book here.

**Practical statistics for data scientists**

### by Peter Bruce and Andrew Bruce

Practical Statistics for Data Scientists is a guide to applying statistical methods to data science with practical code examples and explanations of statistical terms. Aimed at data scientists familiar with the R programming language, this book is a quick reference for understanding how to integrate statistical methods and avoid misusing them. The book covers data structures, datasets, random sampling, regression, descriptive statistics, probability, statistical experimentation, and machine learning. The code is available in both Python and R.

find the book here.

**pattern classification**

### By Richard O Duda

A popular book that explains mathematical formulas and algorithms, Pattern Classification, was first published in 1973 and updated a few years ago. The book explores neural networks, machine learning, and statistical learning using both classic and new methods. It includes examples, case studies, and algorithms to explain specific techniques and historical remarks. Topics covered include Bayesian decision theory, stochastic methods, unsupervised learning and clustering, linear discriminant functions, nonparametric techniques, algorithm-independent machine learning, multilayer neural networks, and nonmetric methods.

find the book here.

**Advanced Engineering Mathematics**

### By Erwin Kreyszig

Originally published in 1962 and updated in 2015, Advance Engineering Mathematics is a popular theoretical choice for engineers, computer scientists, and data scientists to learn more about statistics and practical applications. The book covers differential equations, Fourier analysis, vector analysis, complex analysis and algebra. The latest version of the book examines the use of technology for conceptual problems and projects from the perspective of statistics and advanced mathematics.

find the book here.