The Data Science candidates hold the strong background in statistics and mathematics is the only criteria of being getting selected at Google. Not just Google, other top companies (Amazon, Airbnb, Uber etc) in the world also prefer candidates with strong fundamentals rather than mere know-how in data science. If you are also interested to work with top brands than it is essential to develop your mathematical understanding of data science.
Here, below is the list of books on statistics and mathematics which will help you to connect with data science better.
This is a highly recommended book for practicing data scientists. The focus of this books is kept on connecting statistics concept with machine learning. Hence, you'll learn about all popular supervised and unsupervised machine learning algorithms. R users will get an advantage since the practical aspects of algorithms have been demonstrated using R. In addition to theory, this book also lay emphasis on using ML algorithms in a real-life setting.
This book is an advanced level of the previous book. It is written by Trevor Hastie and Rob Tibshirani, Professors at Stanford University. Their first book ΓΆ??Introduction to Statistical Learning' uncover the basics of statistics and machine learning. This book will introduce you to higher level algorithms such as Neural Networks, Bagging & Boosting, Kernel methods etc. The algorithms have been implemented in R programming.
The author of this book is Alien B Downey. It is based on performing statistical analysis practically in Python. Hence, make sure you've got some basic knowledge of Python before buying this book. It focuses entirely on the understanding real-life influence of statistics using popular case studies. Since stats and math are closely connected, it also has dedicated chapters on the topic like Bayesian estimation.
Did you know the about crucial role of statistics in programming? The author of this book is Norm Matloff, Professor, University of California. This book explains using probabilistic concepts and statistical measures in R. Again, a good practice source for R users. It teaches the art of dealing with probabilistic models and choosing the best one for final evaluation. It is a highly recommended book (especially for R users).
This is a highly recommended book for freshers in data science. The author of this book is William M Bolstad. It's a must-read for people who find mathematics 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 gathering and ends up delivering dedicated chapters on Bayesian statistics.
This book is written by Andy Field, Jeremy Miles, and Zoe Field. I would highly recommend this book to newbies in data science. To start with statistics, this book has great content which goes in-depth detail of its topics. Along with, the statistical concept is explained in conjunction with R which makes it even more useful. It offers a step by step understanding, with a parallel support of interesting practice examples.
This is one of the most recommended books on Linear Algebra. The author of this book is Gilbert Strang, Professor, MIT. Gilbert unique way of delivering knowledge would give you the intuition and excitement to move forward after every chapter. This book will help you to build a strong mathematical foundation for machine learning. It enlists all the necessary chapters such as vectors, linear equations, determinants, eigenvalues, matrix factorization etc in great depth.
Matrix and Data frames are essential components of machine learning. The author of this book is Gene H Golub and Charles F Van Loan. This book provides a nice head start to students with concepts of matrix computations. The author covers most of the important topics such as Gaussian elimination, matrix factorization, lancoz method, error analysis etc. Every chapter is supported by intuitive practice problems. The pseudo codes are available in Matlab.
This is a complete resource to learn the application of mathematics. This is a must-read book for intermediate and advanced practitioners in machine learning. This book is written by Luc Devroye, Laszlo Gyorfi, and Gabor Lugosi. It covers a wide range of topics varying from Bayes error, linear discrimination to epsilon entropy & neural networks. It provides a convincing explanation to complex theorems with section wise practice problems.
If you have an innate interest in learning about the neural network, this should be your place to start. The author of this book is Jeff Heaton. The author has beautifully simplified the difficult concepts of neural networks. This book introduces you to basics of underlying maths in neural networks. It assumes the reader has prior knowledge of algebra, calculus, and programming. It demonstrates various mathematical tools which can be applied to neural networks.
This is probably the most comprehensive book available on mathematics for machine learning users. The author of this book is Erwin Kreyszig. As a matter of fact, this book is highly recommended to college students as well. If you haven't been good at maths till now, follow this book religiously and you should surely see significant improvements in your math understanding. Along with derivations & practice example, this book has dedicated sections of calculus, algebra, probability etc. Definitely, a must read the book for all levels of practitioners in data science.
This cookbook is must have in your digital bookshelf. This isn't exactly a textbook you'd discover, but a quick digital guide on mathematical equations. The author of this book is Matthias Vallentin. After you finish with essentials of mathematics, this book will help you connect various theorem and algorithm quickly with their formulae. It's difficult to derive equations instantly, this book will help you to quickly navigate to your desired problem and solve.