Python vs R | Programming Language for Data Science 2020 | GREYATOM

This is the ultimate smack-down between R and Python. Which is the best language for Data Science?

It's hard to know whether to use Python or R for data analysis. And that’s especially true if you're a newbie data analyst looking for the right language to start with.

But it is possible to figure out the strengths and weaknesses of both languages. One language isn’t better than the other—it all depends on your use case

R Language
Ross Ihaka and Robert Gentleman created the open-source language R in 1995 as an implementation of the S programming language. The purpose was to develop a language that focused on delivering a better and more user-friendly way to do data analysis, statistics and graphical models. At first, R was primarily used in academics and research, but lately the enterprise world is discovering R as well. This makes R one of the fastest growing statistical languages in the corporate world.
One of the main strengths of R is its huge community that provides support through mailing lists, user-contributed documentation and a very active Stack Overflow group. There is also CRAN, a huge repository of curated R packages to which users can easily contribute. These packages are a collection of R functions and data that make it easy to immediately get access to the latest techniques and functionalities without needing to develop everything from scratch yourself.
To end, if you’re an experienced programmer, you probably won't have a hard time getting up to speed with R. As a beginner, however, you might find yourself struggling with the steep learning curve. Luckily, there are many great learning resources you can consult nowadays.
Python Language
Python was created by Guido Van Rossem in 1991 and emphasizes productivity and code readability. Programmers that want to delve into data analysis or apply statistical techniques are some of the main users of Python for statistical purposes.
The closer you get to working in an engineering environment, the more likely it is you might prefer Python. It’s a flexible language that is great to do something novel, and given its focus on readability and simplicity, its learning curve is relatively low.
Similar to R, Python has packages as well. PyPi is the Python Package index and consists of libraries to which users can contribute. Just like R, Python has a great community but it is a bit more scattered, since it’s a general purpose language. Nevertheless, Python for data science is rapidly claiming a more dominant position in the Python universe: the expectations are growing and more innovative data science applications will see their origin here.

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Python vs R | Programming Language for Data Science 2020 | GREYATOM