Best Statistics Books for Data Science

Summary : Demystify statistics for Data Science! This curated list explores 10 best books for beginners and experienced learners alike. Dive into classics like “Naked Statistics” or unveil the secrets of “Think Stats” by Allen B. Downey. Grasp essential concepts from practical applications to statistical learning with R.


If you’re an aspiring Data Scientist, you would want to acquire statistical knowledge from the best authors and statisticians. For this, you need to study and take guidance from books which are the best in the market that would ensure your technical skill development. Thankfully, there is not just one book that can be called the best.

Learning from multiple books would help you gain knowledge on statistical methods based on the proficiency of different authors. If you’re a beginner level Data Scientist or someone who is on the way to become a Data Scientist, there are some of the best Data Science Statistics books for beginners that this blog would review.

For those at the advanced level, you would find this blog post put forward some of the best Data Science top books for experts. Let’s get started! 

Top 10 Statistics Books for Data Science 

Following are the top 10 statistics books for Data Science including Data Science books for beginners and experts:  

How to Lie with Statistics by Darrel Huff

One of the most influential books today, How to Lie with Statistics, would provide you with great knowledge on using different statistical methods in your daily life. The book presents the different concepts in layman terms and ensures that as a beginner, you’re able to understand and use the formulas.

This book divided into two sections whereby the first section includes the methods through which you can make statistical claims; the second section includes the technical aspects including mathematical concepts and theories using statistics and probability. 

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Head first Statistics: A Brain-Friendly Guide Book by Dawn Griffiths 

If you’re a Data Science beginner and want to understand the basics of Statistics, Head Forst Statistics: A Brain Friendly Guide Book is the best one for you. You would become proficient in decision-making abilities and showcase your skills as an efficient Data Scientist.

Though this is a guide suitable for beginners, you need to have a background in statistics to typically understand the concepts of Statistics.

Griffiths has ensured to include examples which comply around everyday life and includes topics like probability, population sampling, hypothesis testing, etc. 

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Think Stats by Allen B. Downey 

This book by Allen B. Downey is one of the most simple and easy-to-understand book on Statistics. Downey includes crucial statistical concepts and methods and explains them using examples which are simple to understand and evaluate as well. M

Moreover, the book is curated in a manner that even a high school graduate can understand the concepts. This implies that even a beginner in Data Science without much technical background might be able to study the book and develop statistical knowledge.

Starting from the basic level topics of multiplication, addition, etc to that of core concepts like probability and sampling, the book covers it all. The book also contains practice exercises using which you would be able to ensure your skill development in statistical analysis.

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An Introduction To Statistical Learning With Applications In R By Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani 

One of the best Data Science books for beginners, Statistical Learning with Applications in R can identified to be a crucial book which includes core concepts in Statitiscs. Several modern techniques in Machine Learning have included within the book along with sequential decision-making as an important topic in the book.

From clustering and regression to Bayesian concepts, you would be able to learn and understand all the core and advanced concepts of Statistics from this book. If you’re a Machine Learning enthusiast with statistical skills but you want to develop your knowledge on programming languages, this is the right book for you.

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Statistics in Plain English by Timothy C. Urdan 

This is one of the best Data Science books for beginners by Timothy C. Urdan which is basically a beginner’s guide to statistical learning. You find the basic level concepts to a mid-level advanced concepts that have been explained in simple terms and explained with real life situations.

Timothy has been able to include with the conceptual ideas a glossary of exercises, resources as well as the solved exercises for you to study and learn. The topics included within this book are simple topics like mean, median, mode to complex topics like probability, and population sampling. 

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Also Find Best Books To Learn Machine Learning For Beginners And Experts

Naked Statistics: Stripping the Dread from the Data – By Charles Wheelan 

Naked Statistics is the book by Wheelan which provides you with a learning pattern whereby you learn to endure statistical analysis based on intuition rather than focusing on mathematical theories. The book covers critical concepts like regression, inference, correlation, etc although the book is far away from minute technicalities.

The book is for advanced level Data Science students and aspirants in the field to make them understand Statistical analysis based on intuitive evaluations. Wheelan believed that while mathematical theories are extremely important in Data Science, it is also important to have minute knowledge on making use of statistical tools effectively. 

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Bayesian Methods For Hackers – Probabilistic Programming and Bayesian Inference, By Cameron Davidson-Pilon 

This book is one of the most complex and conceptual books by Davidson-Pilon whereby it includes Bayesian Methods for conducting complex mathematical analysis. You would be able to learn probabilistic programming using different statistical and programming tools including PyMC Language and NumPy, Scifi and other Python tools.

This book is aimed at improving the knowledge and concepts of Bayesian methods of Statistical and probabilistic programming. However, you need to have efficient statistical knowledge in order to learn the methods of Bayesian. 

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Practical Statistics for Data Scientists – By Peter Bruce and Andrew Bruce 

This book by Peter and Andrew Bruce is based on conducting statistical analysis based on the programming languages. If you are proficient in the R programming language, that can be considered more beneficial than someone who has no knowledge in programming.

The book contains concepts of Data Science related to practical based statistics and includes topics of data structures, descriptive analysis, regression analysis, probability and sampling aspects. Inference related statistical evaluations are also present in order to learn through a complex set of procedures thus, enabling an advanced level learning. 

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Advanced Engineering Mathematics by Erwin Kreyszig 

As the name of the book suggests, this is specifically for students of Advanced Engineering and applied mathematics. This book contains topics which are related to inference, mathematical calculations including calculus, vector, tensor, differential equations, partial differential equations, linear models, etc.

 Kreyszig with this book has been able to focus on the practical implementation of mathematics and engineering in society. This book is especially beneficial for those of who are pursuing graduation or might interested in the Data Science or Data Engineering field. 

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Computer Age Statistical Inference by Bradley Efron and Trevor Hastie 

If you are a Data Scientist who is keen on gaining knowledge regarding the theory behind Machine Learning and statistical inference, then this is the book meant for you. 

You might be able to find certain real-life examples on the theories of Statistical Inference that have been applied through the book and have been presented in terms of practical experiences.

Introduction to statistical inference, regression analysis and models, logistic regression models, etc are focused within the book that would guide one to become a proficient Data Scientist. 

The book also includes clear evaluation on the classification of Bayesian Models thus, providing an advanced level of statistical inference theories for you.

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If you are interested check out: Data Science Course Syllabus Beginners – Course Curriculum

Summing it up!!! 

Hence, from the above post it can be found that these top Data Science Books for Statistics can help you further your knowledge on the subject area. If you are a beginner, the first five books can be highly helpful for you to gain basic level knowledge on probability and sampling.

For advanced level learning and knowledge, you would need to have prior knowledge of statistics, mathematics and even programming languages to understand the conceptual ideas formulated through these books.

Additionally, you might want to consider undertaking a professional course of Data Science online, for which Pickl.AI might be of greater help. 

Pickl.AI’s Data Science course for Professionals and Data Science Job Guarantee Program, ensures that you learn from the basic level of Data Science concepts to those of advanced level under the guidance of expert in-house Data Scientists.

Frequently Asked Questions

What is my Ideal Starting Point if I’m New to Statistics?

If you’re a beginner, consider books like “Naked Statistics” by Charles Wheelan or “Head First Statistics” by Dawn Griffiths. These titles offer a user-friendly approach to core concepts, often using real-world examples to make statistics relatable and engaging.

Are There Books That Focus on Applying Statistics to Data Science?

Absolutely! Titles like “Practical Statistics for Data Scientists” by Peter Bruce and Andrew Bruce or “An Introduction to Statistical Learning with Applications in R” by Gareth James et al. delve deeper into statistical methods specifically used in the Data Science field. These books often explore how to implement these techniques using popular programming languages like R.

Can I Find a Book That Bridges the Gap Between Basic Statistics and Data Science?

Yes, there are books that bridge the gap between foundational concepts and practical applications. “Think Stats” by Allen B. Downey is a great example. It uses Python to explain statistical methods in a clear and engaging way, making it ideal for those who want to transition from the basics to Data Science applications.



  • Asmita Kar

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    I am a Senior Content Writer working with Pickl.AI. I am a passionate writer, an ardent learner and a dedicated individual. With around 3years of experience in writing, I have developed the knack of using words with a creative flow. Writing motivates me to conduct research and inspires me to intertwine words that are able to lure my audience in reading my work. My biggest motivation in life is my mother who constantly pushes me to do better in life. Apart from writing, Indian Mythology is my area of passion about which I am constantly on the path of learning more.