Preface

This is not a book about a specific technology or programming language. This is a book about mathematics. And mathematics is a language. It is the language of science, and so it is the language of data science as well. We can say beautiful things with that language. Just as a piece of great literature is more than a large collection of individual letters, a mathematical equation is more than just a collection of symbols. An equation conveys a way of thinking about a data science problem. It conveys a concept or an idea. If you want to fully exploit the power of those ideas and adapt them to your own data science work, you need to move beyond just recognizing the symbols in an equation and move towards understanding what that equation is really telling you.

Many people are not confident in reading and interpreting mathematical equations and mathematical ideas. And yet, as with great literature, once someone guides us through the nuances and subtexts, their beauty is revealed and becomes obvious. That is what this book aims to do.

This book will not make you an expert in every area of mathematics. Instead, it will give you enough skills and confidence to read and navigate mathematical equations and ideas on your own. We do that by walking you through the core concepts that underpin many data science algorithms – the 15 math concepts of the book’s title. We also do that by walking through those concepts slowly and in detail. I am not a fan of mathematics books that consist solely of theorems, lemmas, and proofs. Instead, this book is unapologetically long-form math. When we introduce an equation, we will explain what the equation tells us, what its implications and ramifications are, and how it connects to other parts of math. We also illustrate those concepts with code examples in Python.

At the end of the book, you will be equipped to look at the math equations of any data science algorithm and confidently unpack what that algorithm is trying to do.