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10 Best Statistics Book for Data Science
Published
15th Sep, 2023
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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.
10 Best Statistics Books for Data Science
Let’s get started with the most popular books for statistics for data science.
1. Think Stats
By Allen B. Downey
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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. The Signal and The Noise: Why most predictions fail but some don’t
By Nate Silver
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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.
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3. 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.
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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.
4. Naked Statistics: Stripping the Dread from the Data
By Charles Wheelan
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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.
5. Practical Statistics for Data Scientists
By Peter Bruce and Andrew Bruce
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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.
6. Computer Age Statistical Inference
By Bradley Efron and Trevor Hastie
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Computer Age Statistical Inference is basically statistics in a time machine. This book takes you on a breathtaking journey of how statistics and its inference have evolved from before to after the introduction of modern-day computers.
The book is divided into three parts:
- Classic Statistical Inference
- Early Computer-Age Methods
- Twenty-First Century Topics
Computer Age Statistical Inference can be considered a statistics textbook for data science. It’s a great read that draws a strong contrast between algorithmic and inferential aspects of statistical analysis.
7. Advanced Engineering Mathematics
By Erwin Kreyszig
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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.
8. Pattern Classification
By Rochard O'Duda
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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.
9. Head First Statistics
By Dawn Griffiths
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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 KnowledgeHut bootcamp for data science, where most of the related questions are answered in an intuitive way.
10. An Introduction To Statistical Learning
By Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
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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.
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Conclusion
The books mentioned in this article are the best statistics books for Data Science. They can help you start with and understand the statistics needed to pursue data science, and make better inferences about the data. I hope you enjoy reading these books and implement the learnings effectively in your Data Science journey.
Frequently Asked Questions (FAQs)
1. What Statistics should I learn for Data Science?
Some of the most important topics you should know for Data Science include probability theory, conditional probability, random variables, central limit theorem, variance and covariance, Gaussian/Normal distribution, confidence intervals, hypothesis testing, regression etc.
2. How do you practise statistics in Data Science?
You can go through one of the statistics books above and start implementing the concepts with the help of programming. You can pick a statistics book that contains code and data examples. This way you can see much of the statistics in action.
3. Which book is best for data science beginners?
Think Stats can be a good option if you are familiar with Python, whereas An Introduction to Statistical Learning can be a great start if you know R programming.
4. What book should I read for Data Science?
There are plenty of books to read from. The answer depends on the area of data science you want to gain knowledge in. Even the above-mentioned books are sufficient to touch upon Data Science basics.
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