Mathematical Topics and Books to Study for Data Science

As discussed in the previous blog post, strong Mathematics is the fundamental requirement of Data Science. Within mathematics the key topics to study are :- Probability and Statistics, Linear Algebra, Optimization and Machine Learning. To judge a Data Scientist’s mathematical skill, interviewers usually ask them specific questions from any one of these specialized topics. Hence, it is essential to have strong understanding on these subjects. These topics are difficult to cover in a single book. This blog post list down some well known and popular books for each of these topics.

Probability and Statistics

  • Probability and Statistics in Engineering – Book by William W. Hines
    This is a great book in statistics which covers wonderful real life problems in engineering to describe the concepts in statistics. This books strongly focus on statistics and covers all the major topics – probability, estimations, distributions, confidence intervals, hypothesis testing, regression and simulation. It is recommended to students with engineering background.
  • Statistics for Business and Economics – Book by Dennis J. Sweeney, Poul Anderson, and Willard Thorp
    This book in comparison is less mathematical and more oriented towards business and economics. In principle, this book can also be the first choice of study.

Machine Learning

  • An Introduction to Statistical Learning – Book by Robert Tibshirani and Trevor Hastie
    With strong background in statistics, this book will introduce into the world off machine learning. The topics presented in the books are easy to understand and discussed topics are implemented on computer using R programming. This book is great to learn machine learning concepts and also to implement them in R.
  • Pattern Recognition and Machine Learning – Book by Christopher Bishop
    One of the most revered books in academia. The author has strong inclination towards Bayesian inference and almost all the chapters includes Bayesian statistics. It is tough book to follow, yet provides you strong knowledge on machine learning. This book is difficult for beginners.

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