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# Top 10 Best Statistics Books for Data Science [For Beginners & Advanced Data Scientist]

## Introduction

Statistics is an indispensable area of study for data scientists. The study of statistics enables one to collect, characterise, comprehend, visualise, and make conclusions about data. A larger statistical perspective is essential for a data scientist. This blog will introduce the top ten best statistics books for data science. These books can assist in understanding the statistical know-how you need to pursue data science and draw accurate conclusions from the data. Learn what each book covers and how it may help bolster your data science career.

## The Importance of Statistics in Data Science

The elements of statistical learning techniques in data science allow data scientists to uncover hidden patterns and relationships in data, leading to more accurate predictions and informed decision-making. Here are a few reasons stating the importance of statistics in data science:

• Ties data to the questions all businesses encounter, irrespective of the sector.
• Plays a vital role in comprehending complicated real-world situations.
• Assists in uncovering important patterns and changes in data.
• Used to create predictions and seek distinct data structures.
• Helps data scientists understand the data science lifecycle.
• The cornerstone of data science and probability
• Handles varied analytical problems in conjunction with probability
• Employed to gain knowledge about the data to make judgments.
• Assists in handling vast, disorganised data.
• Removes irrelevant information and catalogues the important facts in an uncomplicated method.

## Criteria for Selecting the Best Statistics Books for Data Science

Choosing the best book for statistics for data science can be confusing. Here are a few pointers to help you choose. The book should:

• Teach statistics from a data science approach.
• Include the essentials of statistics, including descriptive statistics, probability, and inferential statistics like correlation and regression.
• Complete and detailed
• Incorporate real-life examples and codes.
• Readable and easy to understand.
• Apply to data science and machine learning.
• Recommended by data science specialists.
• Up-to-date with the latest statistical methodologies.
• Suited for both beginners and advanced learners.
• Have good reviews and ratings from readers.

## Top 10 Statistics Books for Data Science

Here is the list of the top 10 best statistics for data science books:

### Naked Statistics: Stripping the Dread from the Data â€“ by Charles Wheelan

This book aims to make statistical concepts understandable and engaging. It is for readers who struggle with statistics or fear the subject. The author avoids bogging readers down with technical details and concentrates on fundamental concepts of statistical problems. The book has received praise for its wit and simplicity and is a popular recommendation. Probability, regression analysis, and hypothesis testing are some topics covered in it.

### Practical Statistics for Data Scientists â€“ by Peter Bruce and Andrew Bruce

This book covers over 50 essential statistical concepts using R and Python. It is an excellent resource for beginners and data science experts since it demonstrates the application of various statistical methodologies from a data science perspective. Probability, hypothesis testing, regression analysis, and machine learning are some areas covered in the book. Additionally, the book provides exercises and valuable examples for readers to practice what they learn. The book thoroughly introduces the fundamental statistical concepts required for data science.

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### Think Stats – by Allen B. Downey

â€˜Think Statsâ€™ introduces probability and statistics for programmers. It is aimed to assist readers in enhancing their grasp of probability and statistics by building and testing code. It teaches users how to run experiments to investigate statistical behaviour and perform statistical analysis computationally using Python.

### Computer Age Statistical Inference – by Bradley Efron and Trevor Hastie

This book was first published by Cambridge University Press in 2016. It provides a course in modern statistical thinking and covers subjects like statistical learning, high-dimensional problems, multiple testing, nonparametric inference, model selection and empirical Bayes methods.

It is aimed at statisticians, data scientists, and academics interested in modern statistical methods and applications. The student edition of the book includes tasks to reinforce their grasp of the subject. It is an excellent resource for learning statistical inference in the computer age.

### Data Mining and Machine Learning â€“ by Mohammed J. Zaki and Wagner Meira Jr.

The book focuses on algorithms and the underlying mathematical principles, presenting the foundations of data analysis, clustering, regression, pattern mining, and classification. It is intended for graduate and senior undergraduate-level machine learning, data mining, and data science courses. It covers fundamental and advanced data mining subjects, discusses the mathematical foundations and the algorithms of data science, contains exercises for each chapter, and gives data, presentations, and other supplemental information on the companion website.

### Statistics In Plain English â€“ by Timothy C. Urdan

This book offers an in-depth discussion of statistics to aid readers in mastering how statistics function and how to interpret them effectively. It covers various statistical methodologies, from fundamental ideas like central tendency and defining distributions to advanced subjects like t-tests, regression, repeated measures ANOVA, and factor analysis. The book is an introductory textbook for beginners who wish to learn about statistics. Currently, it is running in its fourth edition.

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### An Introduction To Statistical Learning – by Gareth James, Trevor Hastie, Daniela Witten, and Robert Tibshirani

This book concisely describes statistical learning, a critical set of approaches for analysing vast and sophisticated data sets. Those who wish to gain expertise using current data analysis tools should read this book. This book’s first edition with R applications (ISLR) was published in 2013, and a second edition followed in 2021.

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### Pattern Classification – by Richard O Duda

The book offers information on how to choose the most practical answer for a particular class of problems and offers a wide range of techniques for pattern classification. Additionally, it provides accompanying algorithms for the generation and display of data. The hardcover book has an inexpensive MATLAB toolbox containing the main pattern classification techniques. The first edition, published in 1973, is now regarded as a standard work on pattern recognition. The second edition contains information on new subjects and provides a comprehensive examination of the use of statistical methods in pattern recognition.

### Introduction to Linear Algebra – by Gilbert Strang

This book discusses orthogonality, determinants, eigenvalues, eigenvectors, vectors, linear equations, vector spaces, and subspaces. The book stands out for its numerous examples, concise explanations, and practical applications. It is commonly used in math, engineering, and science undergraduate classes and the independent study of professionals and researchers.

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### Head First Statistics: A Brain-Friendly Guide – by Dawn Griffiths

This book seeks to make statistics more exciting and fun for readers. It is accessible as an ebook on numerous platforms, including Amazon Kindle and Google Play Books. The book employs engaging and thought-provoking material, including puzzles, anecdotes, quizzes, visual aids, and real-world examples, to educate readers on what they want and need to know about statistics. It covers subjects such as histograms, probability distributions, and chi-square analysis. The book is intended for students, professionals, and anybody interested in statistical analysis.

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## Conclusion & Final Thoughts

Gaining competence in statistics is vital for data scientists to acquire correct insights into the data. Several books are available on statistics for data science that data scientists can choose to increase their knowledge of statistics. These books can help data scientists grasp the statistical knowledge needed to pursue data science and make better judgments about the data. Finding the best-suited book for your work is crucial to boost your data science skills.

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## What are some important statistics concepts for data science?

Data sampling and distributions, probability distributions, hypothesis testing, regression, and Bayesian statistics are a few crucial statistics principles for data science.

## What are some good resources for learning statistics for data science?

There are various effective resources for learning statistics for data science, including books like An Introduction to Statistical Learning and Practical Statistics for Data Scientists and online courses.

## Why is it important to learn statistics for data science?

Statistics is the cornerstone of data science and machine learning. It is the foundation of modern data analysis and interpretation. Since a data scientist needs to use a range of statistical methodologies, having a broad statistical viewpoint is vital.