Before describing why you should learn R, we want to emphasize that you should learn one language as you start learning data science. The reason to focus on one programming language is because you need to focus much more on process and technique, not syntax. You need to learn how to think about data and how to solve problems using the tools of data science. As it turns out, I think that R is the best programming language for doing this.
The popularity of R isn't the only reason to learn R, however.
Ultimately, to really learn data science, you need to learn the "core" skill areas: data manipulation, data visualization, and machine learning.
In selecting a language, you need a language that has significant capabilities in each of these areas. You need tools for performing each of these tasks, as well as resources for learning them in the language you choose.
As noted above, you need to focus much more on process and technique, not syntax. You need to learn how to think about solving problems. You need to learn how to find insight in data.
To do this, you'll need to master the 3 core skill areas of data science: data manipulation, data visualization, and machine learning. Mastering these skill areas will be easier in R than almost any other language.
To be clear, eventually you'll want to learn more programming languages. Just like there's no single best tool in a toolbox, there's no single programming language that's perfect for every data problem you want to solve. Having said that, after you master the core skills in data science in R, you can learn other languages to solve specific problems.
Let's get started with the course on R Programming Language and pave your way to become a Data Scientist