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Top statistics books for data science

Statistics is the crucial component of data science that helps data science learners to capture and convert data patterns into meaningful insights. Data scientists perform statistics to gather, review, analyze, and draw conclusions from data. Consequently, gaining expertise in statistics is essential for data scientists to obtain accurate insights into the data. This article provides an overview of some of the crucial and widely used books for statistics in data science so that you can improve your knowledge of statistics.

Statistics books for data science

Listed below are some essential and most-read statistics books for data science that are available on Amazon.

  1. New Advances in Statistics and Data Science

Written by Ding-Geng Chen, Zhezhen Jin, Gang Li, Yi Li, and Yichua Zhao, The New Advances in Statistics and Data Science book is a collection of selected papers from the 4th ICSA-Canada Chapter Symposium. It also includes the invited articles from established researchers in the field of statistics and data science.

The book covers various topics like methodology development in data science, methodology in the analysis of high dimensional data, features screening in ultra high dimensional data, statistical analysis challenges in sampling, and multivariate survival models. With this book, you can use frontier research methods to tackle research, education, training, consultancy, and more problems.

Link to the book: New Advances in Statistics and Data Science

  1. The Art of Statistics: Learning from Data

Published in March 2019, The Art of Statistics: Learning from Data by Professor David Spiegelhalter provides readers with essential principles needed to derive knowledge from the data. He has used real-life problems in the book to explain conceptual topics and determine how statistics can be applied to make important decisions. Students who want to use statistics to solve or analyze real-life problems can use this book.

The Art of Statistics is one of Professor’s David best-selling books and has been published in more than 11 languages. Professor David is the Chairperson of the Winton Center for Risk and Evidence Communication in the Center for Mathematics Sciences at the University of Cambridge. He was appointed as the President of the Royal Statistical Society in 2017-2018 and became a Non-Executive Director of the UK Statistics Authority in 2020. 

Link to the book: The Art of Statistics: Learning from Data

  1. Naked Statistics: Stripping the Dread from the Data

The Naked Statistics: Stripping the Dread from the Data book by Charles Wheelan mainly focuses on the underlying intuition behind statistical analysis while moving away from the technicalities. 

The author Wheelan highlights concepts such as regression analysis, inference, and correlation. He teaches how data can be manipulated and interpreted by third parties and how it can be explored by data scientists to answer difficult questions. 

Naked Statistics is the best book for people who believe in learning by understanding intuition rather than mathematical theories. It is the perfect book in data science for statistics and probability. 

Link to the book: Naked Statistics: Stripping the Dread from the Data

  1. Practical Statistics for Data Scientists

Written by Peter Bruce and Andrew Bruce, Practical Statistics for Data Scientists is one of the best books for data science. It explains how to apply various statistical methods while avoiding mistakes.

This book’s authors explain how exploratory data analysis is the initial step in data science. They have then covered essential topics such as regression, classification methods, random sampling, principles of experimental designs, and some machine learning techniques that can be learned from data.

This book gives you the statistical perspective that you need to perform the duties of a data scientist effectively. If you have a basic knowledge of R programming language, this book is the best for data science statistics.

Link to the book: Practical Statistics for Data Scientists 

  1. Computer Age Statistical Inference

The Computer Age Statistical Inteferce is a book by Bradley Efron and Trevor Hastie that explores the data analysis and data science revolution with classical inferential Bayesian, Fisherian, and Frequentist theories. 

It guides you on the theories behind machine learning algorithms with in-depth explanations and use-case examples on spam data. This book also covers hypothesis testing, deep learning, empirical Bayes, machine learning, the jackknife and bootstrap, inference after model selection, and Markov chain Monte Carlo.

Computer Age Statistical Inference is divided into Classical Statistical Inference, Early Computer-Age Methods, and Twenty-First-Century Topics. It is a great book that explains statistical analysis’s algorithmic and inferential aspects. 

Link to the book: Computer Age Statistical Inference

  1. High-Dimensional Probability: An Introduction With Applications In Data Science

The High-Dimensional Probability: An Introduction with Applications in Data Science by Roman Vershynin book provides meaningful insights into the behavior of random metrics, random vectors, random subspaces, and objects to identify uncertainty in data. This book is excellent in presenting modern tools of high dimensional geometry and probability in an application-oriented manner with many informative exercises.

This book provides an overview of applications in mathematics, statistics, signal processing, optimization, theoretical computer science, and more. The author has integrated theories, essential tools, and modern applications of high dimensional probability in this book.

Link to the book: High-Dimensional Probability: An Introduction With Applications In Data Science

  1. Probability, Statistics, and Data: A Fresh Approach Using R

Written by Darrin Speegle and Bryan Clair, Probability, Statistics, and Data: A Fresh Approach Using R book provides a fresh approach to calculus-based probability and statistics using R. With this book, you can learn probability through Monte Carlo simulation. In this book, simulation finds answers to difficult probability questions. This book consists of calculus-based mathematical approaches that are connected to experimental computations.

Due to R and simulation in this book, you can get an idea of statistical inference. There are fifty-two datasets included in this book with complementary R package fosdata. Most of these datasets are borrowed from recently published papers to make you work with the current data. In this book, two chapters use powerful tidyverse tools like ggplot2, dplyr, tidyr, and stringr for wrangling data and producing meaningful visualizations.

Link to the book: Probability, Statistics, and Data: A Fresh Approach Using R

  1. Statistics 101: From Data Analysis to Predictive Modeling to Measuring Distribution and Determining Probability, Your essential guide to Statistics

Published in December 2018, Statistics 101 by David Borman is a comprehensive guide to statistics that guides readers on collecting, measuring, analyzing, and presenting statistical data. David Borman provides you with the basics of statistics that are very simple to understand and apply in real-life examples.

With Statistics 101, you can learn probability theories and different distribution concepts to identify data patterns and graphs presenting precise findings. The Statistics 101 book is suitable for students looking to improve their statistical skills and also for professionals to understand how statistics works in businesses. 

David Borman is a working professional at Deutsche bank, TCM Custom House, Morgan Stanley, Phillip Capital, and Merril Lynch. He is into trading mutual funds, stocks, Commodities, and Derivatives. He has worked with the Risk Management Desk of a Singapore Based Future Commission Merchant.

Link to the book: Statistics 101: From Data Analysis to Predictive Modeling to Measuring Distribution and Determining Probability, Your essential guide to Statistics

  1. An Introduction to Statistical Learning

By Gareth James, Daniela Written, Robert Tibshirani, and Trevor Hastie, An Introduction to Statistical Learning give a feasible overview of statistics with examples and applications. This book covers classification, regression, resampling, support vector machines, clustering, and tree-based methods.

An Introduction to Statistical Learning uses an R programming language to implement statistics concepts. Whether you are a technical person or not, this book helps you to understand different statistical methods to analyze data. Therefore, An Introduction to Statistical Learning is the best book for statistics in data science.

Link to the book: An Introduction to Statistical Learning

  1. Statistics without Tears: An introduction for non-mathematicians

Statistics without Tears: An introduction for non-mathematicians was published in July 2018. Written by Derek Rowntree, Statistics without Tears is the perfect book for beginners that explains how statistics work with diagrams.

The book consists of simple concepts of statistics, such as dispersion, correlation, and normal distribution, with relevant examples. The author clearly explains the intuitions behind the statistics concepts in simple words. Derek Rowntree has spent most of his working life in education. He was appointed as the founding member of the Open University and helped students to overcome the challenges of open learning and distance education. 

Link to the book: Statistics without Tears: An introduction for non-mathematicians

  1. Elements of Statistical Engineering: Data Mining, Inference, and Prediction, Second Edition 

The Elements of Statistical Engineering by Trevor Hastie, Jerome Friedman, and Robert Tibshirani describes the key ideas in a wide range of fields like finance, medicine, biology, and marketing in a common conceptual framework. The approach in this book is statistical but focuses more on concepts rather than mathematics.

The book contains many examples with liberal use of color graphics and is a valuable resource for many data scientists. It covers many topics, from supervised machine learning to unsupervised learning, including support vector machines, classification methods, neural networks, and more.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors in statistics at Standford University. Authors Hastie and Tibshirani have developed additive models and written popular books about them. Hastie has also co-developed many statistical modeling software and environment in R/S-PLUS.

Link to the book: The Elements Of Statistical Learning: Data Mining, Inference, And Prediction, Second Edition 

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Manjiri Gaikwad
Manjiri Gaikwad
Manjiri is a computer science graduate from Cummins college of engineering, Pune. She is a simple calm and composed person, who loves to write.

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