We are living in a technology-driven world, which is clearly known to every one of us. But, if I would say we are living in a data-driven world, it won't be a wrong statement indeed. I mean look at the Data! According to an article published in Forbes, we are creating 2.5 quintillion bytes of data every day. For those who do not know about how large this quantity is, I must tell you that it is 1000 to the power 6 bytes, which is a huge huge number!

Businesses are using these large sets of data to actually create a powerful impact on their business. They are requiring talented people who have the relevant skills to manage this data and draw useful insights out of it. It has been recently reported that there are plenty of job opportunities in the field of Data Analysis and Data Science which are left vacant in India. The reason behind these vacant positions was due to the lack of relevant skills possessed by the candidates.

I have come up with this article with a reason to make you understand how R programming Language is very important in the field of Data Science, its salient features which make it the most sought after programming Language in the field of Data Science and Data Analysis. This will leave you with some useful deep insight into the need to learn and gain mastery over this language if you are actually planning your career ahead into Data Science or Data Analysis.

According to Spectrum Survey conducted by IEEE, R ranks 7th among the top 10 Programming Languages of 2018. It is among the most sought after programming languages looked by most of the recruiters today. The reason behind this popularity of R is because of its nature to be used for statistical computing.

If you find difficulty in understanding this term, let me explain it to you.

Suppose you are given a large data set, large means a very large, containing many of the values and figures of a survey or research, from which you need to find the trend. You need to find the useful information from it, analyzing the hidden patterns of what does the data tell you, what are the patterns showed by the data, what are the key factors affecting its growth or any of the question that is related to this large data. Would it be possible for you to do this analysis faster other than a statistical approach? Could you easily infer the hidden patterns without using any statistics? Could you present useful information more effectively without using any pie chart or graphs or visuals? The answer obviously would be No. Statistics will make this analysis faster. Statistical Visualization has its own way to make data more visual and simpler to analyze. It is easier to look at a graph or a pie chart to analyze than to look at the raw data and trying to grasp its meaning. This is where Statistical Computing comes into the major picture,

In Data Science, the data sets are quite complex and large enough to analyze and draw useful insights. You cannot easily draw out useful information without statistical computing. You will need it. You will need a software which is able to help you in this statistical visualization and analysis and R has the salient feature of Statistical computing. It makes much of your task simpler in analyzing these larger complex data sets with the help of Data Visualization.

R has another important feature of it being an open source Programming Language (its source code is available free of cost to the public) which makes it further popular among Data Scientists.