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Python and R for the Modern Data Scientist
book

Python and R for the Modern Data Scientist

by Rick J. Scavetta, Boyan Angelov
June 2021
Beginner to intermediate
196 pages
5h 1m
English
O'Reilly Media, Inc.
Content preview from Python and R for the Modern Data Scientist

Chapter 1. In the Beginning

We would like to begin with a great first sentence, like “It was the best of times, it was the worst of times…” but honestly, it’s just the best of times—data science is flourishing! As it continues to mature, it has begun to splinter into niche topics, as many disciplines do over time. This maturity is the result of a long journey that began in the early days of scientific computing. It’s our belief that knowing some of Python’s and R’s origin stories will help you to appreciate how they differ in today’s environment and, thus, how to get the most out of them.

We’re not going to pretend to be science historians, that niche group of academics who trace the circumstances of great discoveries and personalities. What we can do is offer a highlight reel of where Python and R come from and how that led us to our current situation.

The Origins of R

Whenever I think about R, I’m reminded of FUBU, a streetwear company founded back in the 1990s. The name is an acronym that I immediately fell in love with: For Us, By Us. FUBU meant community; it meant understanding the needs and desires of your people and making sure you served them well. R is FUBU.1 By the end of this chapter, I’m sure you’ll feel the same way. Once we acknowledge that R is FUBU, it starts to make a lot more sense.

We can trace the origins of R right back to the now legendary Bell Laboratories in New Jersey. In 1976, development of the statistical programming language S was ...

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