Preface

Data science has been trending for several years, and demand in the market is now really on the increase as companies, governments, and non-profit organizations have shifted toward a data-driven approach.

Many new graduates, as well as people who have been working for years, are now trying to add data science as a new skill to their resumes. One significant barrier for stepping into the realm of data science is statistics, especially for people who do not have a science, technology, engineering, and mathematics (STEM) background or left the classroom years ago. This book is designed to fill the gap for those people. While writing this book, I tried to explore the scattered concepts in a dot-connecting fashion such that readers feel that new concepts and techniques are needed rather than simply being created from thin air.

By the end of this book, you will be able to comfortably deal with common statistical concepts and computation in data science, from fundamental descriptive statistics and inferential statistics to advanced topics, such as statistics using tree-based methods and ensemble methods. This book is also particularly handy if you are preparing for a data scientist or data analyst job interview. The nice interleaving of conceptual contents and code examples will prepare you well.