Python Data Science Handbook, 2nd Edition

Preface

What Is Data Science?

This is a book about doing data science with Python, which immediately begs the question: what is data science? It’s a surprisingly hard definition to nail down, especially given how ubiquitous the term has become. Vocal critics have variously dismissed it as a superfluous label (after all, what science doesn’t involve data?) or a simple buzzword that only exists to salt resumes and catch the eye of overzealous tech recruiters.

In my mind, these critiques miss something important. Data science, despite its hype-laden veneer, is perhaps the best label we have for the cross-disciplinary set of skills that are becoming increasingly important in many applications across industry and academia. This cross-disciplinary piece is key: in my mind, the best existing definition of data science is illustrated by Drew Conway’s Data Science Venn Diagram, first published on his blog in September 2010 (Figure P-1).

While some of the intersection labels are a bit tongue-in-cheek, this diagram captures the essence of what I think people mean when they say “data science”: it is fundamentally an interdisciplinary subject. Data science comprises three distinct and overlapping areas: the skills of a statistician who knows how to model and summarize datasets (which are growing ever larger); the skills of a computer scientist who can design and use algorithms to efficiently store, process, and visualize this data; and the domain expertise—what we might think of as “classical” ...