Preface

This is the book we wish we’d had when we were teaching ourselves that collection of subjects and skills that has come to be referred to as data science. It’s the book that we’d like to hand out to our clients and peers. Its purpose is to explain the relevant parts of statistics, computer science, and machine learning that are crucial to data science.

Data science draws on tools from the empirical sciences, statistics, reporting, analytics, visualization, business intelligence, expert systems, machine learning, databases, data warehousing, data mining, and big data. It’s because we have so many tools that we need a discipline that covers them all. What distinguishes data science itself from the tools and techniques is the central goal of deploying effective decision-making models to a production environment.

Our goal is to present data science from a pragmatic, practice-oriented viewpoint. We’ve tried to achieve this by concentrating on fully worked exercises on real data—altogether, this book works through over 10 significant datasets. We feel that this approach allows us to illustrate what we really want to teach and to demonstrate all the preparatory steps necessary to any real-world project.

Throughout our text, we discuss useful statistical and machine learning concepts, include concrete code examples, and explore partnering with and presenting to nonspecialists. We hope if you don’t find one of these topics novel, that we’re able to shine a light on one or two other topics that you may not have thought about recently.