Python and R for the Modern Data Scientist: The Best of Both Worlds (Fourth Early Release)
Название: Python and R for the Modern Data Scientist: The Best of Both Worlds (Fourth Early Release)
Автор: Rick J. Scavetta, Boyan Angelov
Издательство: O’Reilly Media, Inc.
Размер: 20.4 MB
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. With this book, data scientists from the Python and R communities will learn how to speak the dialects of each language. By recognizing the strengths of working with both, you’ll discover new ways to accomplish data science tasks and expand your skill set.
Authors Boyan Angelov and Rick Scavetta explain the parallel structures of these languages and highlight where each one excels, whether it’s their linguistic features or the powers of their open source ecosystems. Not only will you learn how to use Python and R together in real-world settings, but you’ll also broaden your knowledge and job opportunities by working as a bilingual data scientist.
— Learn Python and R from the perspective of your current language
— Understand the strengths and weaknesses of each language
— Identify use cases where one language is better suited than the other
— Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows
— Learn how to integrate R and Python in a single workflow
— Follow a real-world case study that demonstrates ways to use these languages together
Who this book is for:
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 vs. them” mentality and towards a unified, productive community. Thus, this book is for those data scientists who see the benefit of expanding their skillset and thereby their perspectives and the value that their work can add to all variety of data science projects.