Introduction
The data scientist selection process at Google gives the highest priority to candidates with a strong background in statistics and mathematics. No solo Google, other major companies (Amazon, Airbnb, Uber, etc.) in the world also prefer candidates with solid fundamentals rather than simple data science knowledge.
If you also aspire to work for such important companies in the future, it is essential that you develop a mathematical understanding of data science. Data science is simply the evolved version of statistics and mathematics, combined with programming and business logic. I have met many data scientists who struggle to explain predictive models statistically.
More than just deriving precision, understand and interpret each metric, the calculation behind that precision is important. Remember, each 'variable’ has a story to tell. So, if not something else, Try to become a great story explorer!
In this article, I have compiled a list of must-read books on statistics and mathematics. I understand, mathematics has no extremes. Therefore, I have included only those books that will help you better connect with data science.
Note: This article mentions books whose access is free of charge by the registered authorities.. On the contrary, a link to the Amazon bookstore is provided.
Stats
Introduction to statistical learning
This is a highly recommended book for practicing data scientists. The focus of these books remains on connecting the concept of statistics with machine learning.. Therefore, learn about all popular supervised and unsupervised machine learning algorithms. R users will get a head start, since the practicalities of the algorithms have been demonstrated using R. Besides the theory, this book also emphasizes the use of ML algorithms in real life settings.
Available: Free download
Elements of statistical learning
This book is an advanced level of the previous book. It is written by Trevor Hastie and Rob Tibshirani, Stanford University professors. His first book, 'Introduction to statistical learning', discover the basics of statistics and machine learning. This book will introduce you to higher-level algorithms, as neural networks, bagging and reinforcement, kernel methods, etc. The algorithms have been implemented in R programming.
Available: Free download
Think about the statistics
The author of this book is Alien B Downey. It is based on performing statistical analysis practically in Python. Therefore, make sure you have some basic knowledge of Python before purchasing this book. Focuses completely on understanding the influence of statistics in real life using popular case studies. Since statistics and mathematics are closely related, it also has chapters dedicated to topics such as Bayesian estimation.
Available: Buy on Amazon
From algorithms to Z scores
Did you know about the crucial role of statistics in programming? The author of this book is Norm Matloff, professor at the University of California. This book explains the use of probabilistic concepts and statistical measures in R. One more time, a source of good practice for R users. Teaches the art of dealing with probabilistic models and choosing the best one for the final evaluation. It is a highly recommended book (especially for R users).
Available: Free download
Introduction to Bayesian Statistics
This is a highly recommended book for beginners in data science. The author of this book is William M Bolstad. It's a must read for people who find math boring. Having been written in a conversational style (rare to find math this way), this book is a great introductory resource on statistics. It begins with scientific methods of data collection and ends with chapters dedicated to Bayesian statistics..
Available: Free download
Discovering statistics using R
This book is written by Andy Field, Jeremy Miles and Zoe Field. I would highly recommend this book to data science newbies. To start with the statistics, This book has great content that delves into the details of its topics. With, the statistical concept is explained together with R, which makes it even more useful. Offers a step-by-step understanding, with a parallel support of interesting practical examples.
Available: Buy on Amazon
Math
Introduction to linear algebra
This is one of the most recommended books on linear algebra. The author of this book is Gilbert Strang, teacher, WITH. Gilbert's unique way of passing on knowledge would give him the intuition and excitement to move on after each chapter.. This book will help you build a solid mathematical foundation for machine learning.. List all the necessary chapters, as vectors, linear equations, determinants, eigenvalues, array factorization, etc. in great depth.
Available: Buy on Amazon
Matrix computing
Data Frames and Arrays are Essential Components of Machine Learning. The author of this book is Gene H Golub and Charles F Van Loan. This book provides a good advantage to students with the concepts of matrix calculations. The author covers most of the important topics like Gaussian elimination, matrix factorization, the lancoz method, error analysis, etc. Each chapter is backed by intuitive practical problems. Pseudocodes are available in Matlab.
Available: Free download
A probabilistic theory of pattern recognition
This is a complete resource for learning the application of mathematics. This is a must-read book for intermediate and advanced machine learning professionals. This book is written by Luc Devroye, Laszlo Gyorfi y Gabor Lugosi. Covers a wide range of topics ranging from bug to bayes, linear discrimination to epsilon entropy and neural networks. Provides a convincing explanation of complex theorems with smart practice problems from section.
Available: Free download
Introduction to neural network mathematics
If you have an innate interest in learning about neural networks, this should be your place to start. The author of this book is Jeff Heaton. The author has wonderfully simplified the difficult concepts of neural networks. This book introduces you to the basic concepts of the mathematics underlying neural networks. It is assumed that the reader has previous knowledge of algebra, calculation and programming. Demonstrates various mathematical tools that can be applied to neural networks.
Available: Buy on Amazon
Advanced Engineering Mathematics
This is probably the most comprehensive book available on math for machine learning users.. The author of this book is Erwin Kreyszig. In fact, This book is also highly recommended for college students. If you haven't been good at math so far, follow this book religiously and you are sure to see significant improvements in your understanding of mathematics. Along with the leads and the practice example, this book has dedicated calculus sections, algebra, probability, etc. Definitely, a must-read book for all levels of data science professionals.
Available: Free download
Cookbook on Probability and Statistics
This cookbook is a must in your digital library. This is not exactly a textbook that you would discover, but a quick digital guide to mathematical equations. The author of this book is Matthias Vallentin. Once you are done with the basics of math, this book will help you to connect various theorems and algorithms quickly with their formulas. It is difficult to derive equations instantly, this book will help you quickly navigate to your desired problem and solve it.
Available: Free download
Additional Resources
Are you bored of reading too much? Here is a list of tutorials (videos) / highly recommended resources on math and statistics. They are FREE to access.
- Complete Linear Algebra Course by MIT
- Complete multivariable calculus course by MIT
- Statistical Learning from Stanford University
- Math at Khan Academy
- Complete probability cheat sheet
Final notes
The books listed in this article are selected based on their reviews and the depth of the topics covered.. This is not an exhaustive list of books. But, I found it almost too easy to get confused when deciding 'where to start?’ In such situations, it is advisable to start with this list.
In this article, I have listed some of the most useful books on statistics and machine learning. It has been found that people tend to neglect these issues in pursuit of quick success. But that's not the right way. Therefore, if your goal is long-term success in data science, make sure you learn how to create stories from math and statistics.
Have you read any of these books ? Which math and statistics book has helped you the most? Share your suggestions / reviews in the comment section below.
