Python and R for the Modern Data Scientist: The Best of Both Worlds 159994

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This book aims at data scientists at the intermediate stage of their careers. As such, it doesn’t attempt to teach data science. Nonetheless, early-career data scientists will also benefit from this book by learning what’s possible in a modern data science context before committing to any topic, tool, or language.

Our goal is to bridge the gap between the Python and R communities. We want to move away from a tribal, “us versus them” mentality and toward a unified, productive community. Thus, this book is for those data scientists who see the benefit of expanding their skill set and thereby their perspectives and the value that their work can add to all variety of data science projects.

It’s negligent to ignore the powerful tools available to us. We strive to be open to new, productive ways of achieving our programming goals and encourage our colleagues to get out of their comfort zone.

Prerequisites

To obtain the best value from this book, we assume the reader is familiar with at least one of the main programming languages in data science, Python and R. A reader with knowledge of a closely related one, such as Julia or Ruby, can also derive good value.

Basic familiarity with general areas of data science work, such as data munging, data visualization, and machine learning is beneficial, but not necessary, to appreciate the examples, workflow scenarios, and case study.

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: It’s an open design process that can be evaluated, understood, and optimized.

Generalizable: Its fundamental tools and concepts are applicable to many domains.

Outward looking: It incorporates, is informed by, and is influenced by developments in other fields.

Ethical and honest: It’s people-oriented. It takes best practices for ethical work, as well as a broader view of its consequences, for communities and society, into account. We avoid hype, fads, and trends that only serve short-term gains.

However the actual job description of a data scientist evolves in the coming years, we can expect that these timeless principles will provide a strong foundation.

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