14 Best Statistics Book for Data Science in 2025
By Rohit Sharma
Updated on Mar 19, 2025 | 13 min read | 19.5k views
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Statistics is at the core of Data Science and Machine Learning. It’s the basis of modern-day analysis and interpretation of data. As a data scientist, your job is to apply various statistical methods and thus it's imperative to have a deeper statistical perspective. For that, it’s good to keep a statistics book of data science handy.
But which is the best statistics book for data science? The good news is that there isn’t just one but many books on statistics for data science that you can start reading today and sharpen your statistics skills.
14 Best Statistics Books for Data Science
Let’s get started with the most popular statistics books for data science.
1. Think Stats
By Allen B. Downey
Think Stats is one of the best books on statistics for Data Science. It’s a great book for beginners having knowledge in Python programming. The book starts by explaining the various concepts of exploratory data analysis in detail. It then talks about distributions and distribution functions in statistics. Finally, it covers advanced topics like hypothesis testing, regression and time series analysis.
Thinks Stats is definitely one of the best statistics books for data science beginners and will give you a good understanding of underlying statistics for data science. But make sure you have a good hold on Python programming before you pick this one as your first statistics data science book because it contains many code examples in Python.
2. Introductory Statistics
| Author Name | Barbara Illowsky, De Anza College Susan Dean, De Anza College |
| Year of release and version | Sep 19, 2013 |
| Good Reads Rating | 2.74/5 |
| Publisher Info | Introductory Statistics is published by OpenStax. Based at Rice University, OpenStax is committed to increasing educational access by offering high-quality, publicly licensed textbooks that are free in digital forms and reasonably priced in print. |
Book Info:
Descriptive statistics, probability, random variables, sampling distributions, hypothesis testing, regression, and chi-square tests are among the basic ideas covered in "Introductory Statistics." Students are helped to comprehend and use statistical techniques by the activities and real-world situations.
Key Takeaways:
- Free and accessible : Completely free as an open educational resource, saving students money.
- Broad Content: Complete treatment of fundamental statistical ideas and techniques.
- Real-world examples and practical learning tasks are included.
- Superior Quality Experts who have written and peer-reviewed it guarantee accuracy and dependability.
3. The Signal and The Noise: Why most predictions fail but some don’t
By Nate Silver
The Signal and the Noise is yet another great statistics book for data science. It even reached New York Time Best Sellers list within a week of its first print. The author of this book, Nate Silver has explained the practical art of mathematical model building using statistics and probability using his own learnings.
He explains how to distinguish ‘true signals’ from noisy data, mistakes to avoid, the prediction paradox, etc. using his real-life experiences and some successful forecasts in different areas.
The Signal and the Noise is probably the best book for statistics for data science especially if you want to learn from real-life experiences and examples. Of course, there are many other ways to learn like joining a bootcamp for Data Science but reading the best book to learn statistics for data science gives you a different edge.
Know more about how to become a dependable data scientist.
4. Introduction to Modern Statistics
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| Author Name | Mine Çetinkaya-Rundel and Johanna Hardin |
| Year of release and version | 2021 |
| Good Reads Rating | 3.83 /5 |
| Publisher Info | A non-profit called OpenIntro is dedicated to enhance education by making excellent, widely available textbooks available under open license. Digitally, their materials are free, and print copies are reasonably priced. The goal of OpenIntro is to create a community of teachers and students who may modify and exchange educational resources, so advancing more accessibility and affordability in education. |
Book Info:
"Introduction to Modern Statistics" covers fundamental and modern statistical methods, with a strong emphasis on data exploration, regression modeling, and inference. It is structured into six sections:
- Introduction to data
- Exploratory data analysis
- Regression modeling
- Foundations of inference
- Statistical inference
- Inferential modeling
Key Benefits and Takeaways:
- Exploratory Data Analysis: Emphasizes visualization and summarization of multivariable relationships.
- Simulation-Based Inference: Introduces randomization and bootstrapping techniques.
- Modern Software Integration: Features R tutorials and labs to apply statistical concepts using modern software tools.
- Accessible Learning: Free online resources, interactive tutorials, and affordable print options make the content widely accessible (IMS1 Stats) (Open Textbooks AIMath) (OpenIntro).
5. Statistics in Plain English
By Timothy C. Urdan
Statistics in Plain English as the name suggests attempts at translating the nuances of statistics into simple English. A different statistical technique is described in each chapter with a short description of the topic and also when it should be used.
Ranging from basics like central tendency and distributions to advanced concepts like T-tests, regression, ANOVA, etc, this book covers the fundamentals of statistics in-depth and with examples. The book also provides links to various useful tools and resources.
Statistics in Plain English is definitely a great pick as a statistics book for data science.
6. Computational and Inferential Thinking
| Author Name | Ani Adhikari and John DeNero |
| Year of release and version | 2017 |
| Good Reads Rating | 3.88/5 |
| Publisher Info | The book is published by the University of California, Berkeley and is available online for free under a Creative Commons license. |
Book Info:
"Computational and Inferential Thinking: The Foundations of Data Science" is a comprehensive introductory text for data science. It covers fundamental concepts in data processing, visualization, and statistical inference using modern programming tools. The book emphasizes practical data analysis skills using Python and real-world datasets.
Key Takeaways:
- Accessible to Beginners: Designed for students without prior experience in computing, calculus, or linear algebra.
- Practical Application: Focuses on hands-on data analysis, utilizing Python and real-world data sets.
- Foundational Concepts: Provides a solid foundation in data science, covering topics like visualization, hypothesis testing, regression, and machine learning.
- Free Resource: Available online for free, making it an accessible educational resource for all learners.
7. Probabilistic Programming and Bayesian Methods for Hackers
| Author Name | Cameron Davidson-Pilon |
| Year of release and version | 2015 |
| Good Reads Rating | 4.12/5 |
| Publisher Info | The book is published by Addison-Wesley as part of their Data and Analytics series. It is also available through O'Reilly Media's learning platform. |
Book Info
Overview: "Probabilistic Programming and Bayesian Methods for Hackers" is a comprehensive guide that introduces Bayesian inference through practical, real-world examples using the Python library PyMC. The book aims to bridge the gap between theoretical Bayesian statistics and practical application, making complex concepts accessible through code and examples.
Key Takeaways:
- Hands-On Approach: The book uses practical examples to teach Bayesian inference, making it accessible for practitioners.
- PyMC Library: Focuses on using the PyMC library for probabilistic programming, highlighting its application in solving real-world problems.
- Incremental Learning: Concepts are introduced in small, manageable steps, allowing readers to build their understanding gradually.
- Open-Source and Collaborative: Emphasizes the importance of open-source tools and collaboration, providing code and resources on GitHub.
8. Naked Statistics: Stripping the Dread from the Data
By Charles Wheelan
If you slept through your statistics lessons, Naked Statistics can be your champion and lifesaver. The book focuses mainly on the underlying intuition behind statistical analysis while stripping away the technicalities.
The author, Wheelan throws light on concepts like inference, regression analysis, and correlation. He shows how data can be manipulated and misinterpreted by careless parties, and how the same data is being brilliantly exploited by researchers and experts to answer difficult questions.
Naked Statistics can prove to be the best book for statistics and probability for data science for those who believe in learning by understanding intuition rather than mathematical theories. Sometimes we seek the same kind of learning when we are searching for the best data science courses in India. Yes, the mathematical formulations are important but so is the innate knowledge to use the statistical tools at hand effectively.
9. Computer Age Statistical Inference
| Author Name | Bradley Efron and Trevor Hastie |
| Year of release and version | 2016 |
| Good Reads Rating | 4.38/5 |
| Publisher Info | Cambridge University Press Cambridge University Press is a leading academic publisher, known for producing high-quality scholarly works. Established in 1534, it is the world's oldest publishing house and operates as part of the University of Cambridge. |
Book Info:
Computer Age Statistical Inference explores the evolution and integration of statistical methodologies with computational advancements. It covers topics from classical inference theories to modern machine learning algorithms, focusing on the impact of increased computational power on statistical practices.
Key Takeaways:
- Comprehensive Coverage: Detailed exploration of classical and modern statistical methods, including Bayesian and frequentist approaches.
- Practical Applications: Real-world examples demonstrating the use of statistical methods in big data and machine learning.
- Historical Context: Insight into the historical development of statistical methods and their adaptation to computational advances.
- Future Directions: Speculation on the future of statistics and data science in light of ongoing technological progress.
10. Practical Statistics for Data Scientists
By Peter Bruce and Andrew Bruce
How direct and apt could be a book title as it is here. Practical Statistics for Data Scientists is one of the best statistics books for data science. It explains how to apply a variety of statistical methods to data science while avoiding the most common mistakes.
The authors, Peter and Andrew begin the book by explaining how exploratory data analysis the first step in Data Science is. They then cover important topics like random sampling, principles of experimental design, regression, classification techniques, and finally some statistical machine learning methods that learn from data.
Practical Statistics for Data Scientists certainly gives you the statistical perspective that one needs to perform the duties of a Data Scientist effectively. If you have knowledge of R programming, this book can be your best book for Data Science statistics.
11. Advanced Engineering Mathematics
By Erwin Kreyszig
Advanced Engineering Mathematics has been a popular choice among computer engineers and data scientists. The book covers topics like differential equations, Fourier analysis, linear algebra, vector calculus, optimization, graphs, etc.
The updated version of this book even explores the usage of technology for solving conceptual problems using statistics and advanced mathematics. Advanced Engineering Mathematics can also be taken as one of the most trusted and best statistics textbooks for data science.
12. Pattern Classification
By Rochard O'Duda
Pattern Classification is an easy-to-follow book and introduces a lot of research done in statistical machine learning and pattern recognition. It’s well written and is a great statistics book for data science.
Pattern Classification includes case studies, examples, and algorithms to explain various techniques and concepts. It covers neural networks, machine learning and statistical learning with both conventional and new day methods.
Some of the important topics covered in Pattern Classification are Bayesian decision theory, stochastic methods, unsupervised learning and clustering, non-parametric techniques, algorithm independent machine learning, and non-metric methods.
13. Head First Statistics
By Dawn Griffiths
Head First Statistics is a great probability and statistics book for data science. It teaches you statistics through interactive and engaging material. It’s full of stories, puzzles, visual aids, quizzes, and real-world examples.
This book helps you get a solid hold on statistics in such a way that you can understand the underlying key points and actually use them. Because of its friendly and easy to understand content, it's also recommended for students learning statistics during their college.
One of the good thighs about Head First is that it answers a lot of questions. In Fact, most of the chapter names are in the form of questions. This book reminds me of upGrad bootcamp for data science, where most of the related questions are answered in an intuitive way.
14. An Introduction To Statistical Learning
By Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
An Introduction to Statistical Learning gives a feasible overview of statistics, teaching some of the most important modelling techniques along with examples and applications.
Some of the topics that are covered in this book are regression, classification, resampling methods, tree-based methods, support vector machines, clustering etc. The book uses R programming to facilitate the practical implementation of statistical concepts.
Whether you are a statistician or a non-statistician, this book helps you use advanced statistical learning techniques to analyse data. And therefore An Introduction to Statistical Learning is one of the best statistics books for Data Science.
Conclusion
The books mentioned in this article are the best statistics books for data science. They provide a strong foundation in statistical concepts, helping you analyze data effectively and make informed decisions. Whether you're a beginner or an advanced learner, these books will enhance your understanding of statistics in data science.
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