As Big Data continues to grow in importance at Software as a Service (SaaS) companies, the field of Big Data analytics is a safe bet for any professional looking for a fulfilling, high-paying career.
If you’re considering starting or advancing your career in the field of Big Data and data science, we’ve described popular programming languages you might want to learn to give that career move a boost with Best R Books
Why Learn R?
A good data scientist is a passionate coder-slash-statistician, and there’s no better programming language for a statistician to learn than R. The standard among statistical programming languages, R is sometimes called the “golden child” of data science. It’s a popular skill among Big Data analysts, and data scientists skilled in R are sought after by some of the biggest brands, including Google, Facebook, Bank of America, and the New York Times.
Also, R’s commercial applications increase by the minute, and companies appreciate its versatility. If you’re intrigued and want to know why you should learn R, here are a few more reasons why you should add R to your skillset:
1. R is Open-source and Freely Available
Unlike SAS or Matlab, you can freely install, use, update, clone, modify, redistribute and resell R. This saves companies money, but it also allows for easy upgrades, which is useful for a statistical programming language.
2. R is Cross-platform Compatible
R can be run on Windows, Mac OS X, and Linux. It can also import data from Microsoft Excel, Microsoft Access, MySQL, SQLite, Oracle, and other programs.
3. R is a Powerful, Scripting Language
As such, R can handle large, complex data sets. R is also the best language to use for large, resource-intensive simulations, and it can be used on high-performance computer clusters.
4. R Has Widespread Acclaim
With an estimated 2 million users, R is one of the top programming languages of 2017.
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5. R is Highly Flexible and Evolving
Many new developments in statistics first appear as R packages.
6. Publishers Love R
R integrates easily with document preparation systems like LaTeX. That means statistical output and graphics from R can be embedded into word-processing documents.
7. R Has a Vast, Vibrant Community and Resource Bank
With a global community of passionate users who regularly interact on discussion forums and attend conferences. Also, about 2000 free libraries are available for your unlimited use, covering statistical areas of finance, cluster analysis, high-performance computing, and more.