Learning to Love Data Science

Preface

I first heard the term “data science” in 2011, during a conversation with David Smith of Revolution Analytics. David led me to Drew Conway, whose data science Venn diagram (reproduced with his permission in Figure 1-1) has acquired the legendary status of an ancient rune or hieroglyph.

Like its cousin, “big data,” data science is a fuzzy and imprecise term. But it gets the job done, and there’s something appealing about appending the word “science” to “data.” It takes the sting out of both words. As a bonus, it enables the creation of another wonderful and equally confusing term, “data scientist.”

Confusing is the wrong word. Redundant is a better choice. Science is inseparable from data. There is no science without data. Calling someone a “data scientist” is like calling someone a “professional Major League Baseball player.” All the players in Major League Baseball are paid to play ball. Therefore, they are professionals, no matter how poorly they perform on any given day at the ballpark.

That said, the term “data scientist” suggests a certain raffish quality. Indeed, the early definitions of data science usually included hacking as a foundational element in the process. Maybe that’s why so many writers think the term “data science” is sexy—it conveys a sense of the unorthodox. It requires ingenuity, fearlessness, and deep knowledge of arcane rituals. Like big data, it’s shrouded in mystery.

That’s exactly the sort of thinking that gets writers excited and drives editors crazy. Imprecise definitions aside, there’s an audience for stories about data science. That’s the reason why books like this one are published: They feed our need for understanding something that seems important and yet resists easy explanations.

I certainly hope you find the contents of this book interesting, entertaining, and educational. This book won’t teach you how to become a data scientist, but it will give you fairly a decent idea of the ways in which data science is fundamentally altering our world, for better and for worse.

As you might have already guessed, the main audience for this book isn’t data scientists, per se. I think it’s safe to assume they already love data science, to one degree or another. This book is written primarily for people who want to learn a bit about data science but would rather not sign up for an online class or attend a lecture at their local library.

Careful readers will notice that I rather carelessly use the terms “data science” and “big data” interchangeably, like the way some people use the terms “Middle Ages” and “Medieval Period” interchangeably. I am guilty as charged, and I hope you can forgive me.

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Acknowledgments

This book is a work of journalism, not science. It’s based on the aggregated wisdom of many sources, interviewed over the course of several years. All the sources cited in the original reports had the opportunity to review what I’d written about them prior to publication, which I think is a fair practice.

A long time ago, journalists invented an early form of crowdsourcing. We called it “multiple sourcing.” Back in the old days, our gruff editors would reflexively spike “one-source” stories. As a result, we learned quickly to include quotes and supporting information from as many sources as possible. Multiple sourcing was also a great CYA (cover your ass) strategy: if you wrote something in a story that turned out to be incorrect, you could always blame the sources.

This book would not have been possible without the cooperation of many expert sources, and I thank them profusely for generously sharing their time and knowledge.

I owe special thanks to Mike Loukides and the wonderfully talented group of editors at O’Reilly Media who worked with me on this project: Holly Bauer, Marie Beaugureau, Susan Conant, and Timothy McGovern. Additionally, I am grateful for the support and guidance provided by Edith Barlow, Greg Fell, Holly Gilthorpe, Cornelia Lévy-Bencheton, Michael Minelli, William Ruh, Joseph Salvo, and Amy Sarociek. Thank you all.