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7 Best Books to Learn Statistics and Mathematics For Data Science

  • Difficulty Level : Basic
  • Last Updated : 24 Oct, 2021

Data Science is an incredible field that deals with enormous volumes of data using advanced techniques to derive meaningful information. It has dominated all the industries of the world like healthcare, finance, automobile, manufacturing, education, and many more. As per the survey, it is predicted that the Data science domain will witness a major hike of 27.9 percent in employment by 2026. It offers lucrative career opportunities with an insanely high package and global exposure for those with the right skill set.


To ace in the field of data science you need to acquire the following skills:

  • Mathematical fundamentals
  • Quick data manipulation techniques
  • Mathematical creativity
  • Understanding of statistical principles

Of course, there are other skills required to attain perfection in the field of data science. So you need to surround yourself with the best resources to dig deeper into this domain. There is nothing more perfect than reading books to get an extensive view of data science. Continue reading this blog to get the information about the 7 best books to learn statistics and mathematics for data science.

1. Pattern Classification

This is an amazing mathematics study book written by Richard O Duda. The first edition was published in 1973 and later updated in 2000. This book comes with fantastic text formatting that enhances the memorization of algorithms. It is embedded with hot topics like neural networks, machine learning, and statistical learning. The concepts covered in this book are:

  • Bayesian Decision Theory
  • Nonparametric Techniques.
  • Linear Discriminant Functions
  • Unsupervised Learning and Clustering.
  • Stochastic Methods
  • Algorithm-Independent Machine Learning.
  • Multilayer Neural Networks.
  • Non-Metric Methods.

About the author: Richard O Duda is serving as a professor of Electrical Engineering. He is widely known for his contribution to sound localization and pattern recognition.

2. Introduction to Linear Algebra

This is truly the best book that presents linear algebra in the easiest possible way. It is designed in an extremely concise and readable format. Some of the fantastic concepts that this book includes are:

  • Matrix analysis
  • Cryptography
  • Probability and statistics
  • Professional level algorithms
  • Codes in MATLAB, Julia, and Python

About the author: Gilbert Strang is currently a Professor of mathematics at MIT and has written six amazing books.

3. Naked Statistics: Stripping the Dread from the Data

This book is compiled in an extremely realistic tone that makes statistics come alive. The book progresses quite slowly from basic concepts like normal distribution to complex data analysis algorithms. The book is enriched with astonishing concepts in an incredibly different way that makes statistics easy to understand and grasp.

About the author: Charles Wheelan is a professor, speaker, and founder of Unite America. So far he has authored eleven globally renowned books.

4. How to Lie with Statistics

This is a really good book to clear your basics. It is like a compact set enriched with an abundance of knowledge. The author clarifies concepts like correlation, regression, and inference. He further explains how carelessness can manipulate data and how statistical graphs can be used to discover the reality. The book is quite old but the concepts are valid to date. It is the book on which generations of learners have relied like an old friend.

About the author: Darrell Huff was a renowned author who has written at least sixteen books. His books have been translated into nearly twenty-two languages.

5. Head First Statistics: A Brain-Friendly Guide

This is a popular book that explains everything in a storytelling manner. This book covers:

  • Descriptive statistics like mean, mode, median, etc.
  • Probability distribution: which includes binomial distribution, normal distribution, Poisson distribution, and many more.
  • Inferential statistics like correlation, hypothesis testing, etc.

Every topic is explained with the help of real-world examples to foster your learning experience. This is just the best option if you want to enhance your basics of statistics.

About the author: Dawn Griffiths has experience of nearly twenty years in the IT sector. So far she has authored four popular books.

6. Advanced Engineering Mathematics

This is a well-known book in the field of data science and machine learning. It is the perfect option for learning new skills and understanding basic concepts. This book includes topics like differential equations, Fourier analysis, vector analysis, Complex analysis. Further, it covers precise mathematics concepts like partial differential equations, and linear algebra with outstanding exercises to enhance your learning experience.

About the author: Erwin Kreyszig was an applied mathematician and a professor. He is well known for his contribution to the field of non-wave replicating linear systems.

7. Practical Statistics for Data Scientists

This is a great option if you have prior knowledge of python or R. This book covers amazing concepts like:

  • Exploratory data analysis
  • Data sampling and distribution
  • Statistical experiments
  • Significance testing
  • Statistical machine learning methods
  • Regression and prediction

And many more interesting concepts. The best thing is that the code is available in both Python and R.

About the author: Peter Bruce is a founder of The Institute for statistics education and the author of several amazing books and Andrew Bruce has more than 30 years of experience in the field of statistics and data science. Together they have authored this globally renowned book.

Conclusion: There are thousands of books available to enhance your data science skills but you don’t need to read them all. In this blog, we have carefully selected the best books to learn statistics and mathematics for data science. A few more reference books that can be helpful are Introduction to Probability by Joseph K. Blitzstein and Jessica Hwang, The Nature of Statistical Learning Theory by Vladimir Vapnik, The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Friedman, etc. 

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