Data Science Training- A New Digital Era

 “R” only appears like a humorous name for a language until you realize that more than half the alphabet has been used up for one-letter programming language names. And when you learn that “R” is just an implementation of another language called “S”. R  is named partly after the first names of the first two R authors and partly as a play on the name of “S”.R is discovered by Ross Ihaka and Robert Gentleman and S stand for Statistical programming language.

While we talk about Data Science there are few popular languages which are taught by every institute or training centers that are- Python and R programming language. These two are the default part of the Data Science Course and holds a big share. R has become the hot systematic programming tool of choice for data scientists in every industry from insurance to banking to marketing to pharmaceutical development etc.

For data scientists, R bids a multitude of features making statistical analysis of large data sets simple:

• Linear and non-linear modeling
• Time-series analysis
• Clustering
• Easy extensibility and interfaces to other programming languages
• Sizable shared code package repository

R has a sturdy Integrated Development Environment (IDE) reachable in R Studio and is accessible from a number of scripting languages widely used in the data science community. Anyone seeing a career in data science is going to need more than a fleeting familiarity with R language.

The main aim of any programming language is to jumble the numbers and text such way that they can add value to them in order to give them some meaning. Complexity rises and falls until they achieve something useful of it, and that’s the reason why programming language is equipped with a lot of other internal support tools. But certain languages are designed easier to use with definite tasks. And R is all about data manipulation and visualization. From Scheme, R adopts lexical scoping and a more object-friendly syntax.

Other high-level programming languages are completely skillful of applying the features and functions of R, but all require additional coding to do so. With R, almost every tool a data scientist might need to manipulate and evaluate structured data is included. Apt for both techies and non-techies with its numerous ways, R is definitely worth learning not only for professional level but for self-use too.

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