Python and R for the Modern Data Scientist

Preface

Why We Wrote This Book

We want to show data scientists why being more aware, informed, and deliberate about their tools is an optimal strategy for increased productivity. With this goal in mind, we didn’t write a bilingual dictionary (well, not only—you’ll find that handy resource in the Appendix). Ongoing discussions about Python versus R (the so-called “language wars”) have long since ceased to be productive. It recalls, for us, Maslow’s hammer: “if all you have is a hammer, everything looks like a nail.” It’s a fantasy worldview set in absolutes, where one tool offers an all-encompassing solution. Real-world situations are context-dependent, and a craftsperson knows that tools should be chosen appropriately. We aim to showcase a new way of working by taking advantage of all the great data science tools available, regardless of the language they are written in. Thus we aim to develop both how the modern data scientist thinks and works.

We chose the word modern in the title not just to signify novelty in our approach. It allows us to take a more nuanced stance in how we discuss our tools. What do we mean by modern data science? Modern data science is:

Collective

It does not exist in isolation. It’s integrated into wider networks, such as a team or organization. We avoid jargon when it creates barriers and embrace it when it builds bridges (see “Technical Interactions”).

Simple

We aim to reduce unnecessary complexity in our methods, code, and communications.

Accessible ...