Analytics is all the rage right now. There is an unprecedented surge in the data generated day in and day out, making data analysis a matter of paramount concern. This data might seem chaotic and unmanageable to the average person, but for an analyst it’s like hitting the jackpot! Every crucial decision that major companies and organizations take today is often the result of an exhaustive analysis of carefully mined data.
In the digital age where exabytes of data is put out every day, it can seem like a Herculean task to extract and extrapolate useful information that will help in critical business decision making. This is where the superheroes of Planet Analytics (if you will) step in. Enter SAS & R.
SAS, short for Statistical Analysis System, is an enterprise software suite developed by the SAS institute. It finds application in advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics.
Humorously called ‘the granddaddy of analytics’, it has been around for 40 years and is one of the most popular analytics software out there.
Up until fairly recently, it commanded a market share of close to 75% of all analytics software used by companies for all their data analysis needs. Even with the high cost of obtaining a license to implement SAS, many mid-size and established organizations swear by it.
While SAS has been getting all this glorious attention, R has been slowly but steadily preparing to step into the game, leaving behind university classrooms and computer science labs.
R has been, and rightly so, called the people’s champion of analytics software for the simple reason that it is open source.
What open source means is, it is basically free to use by anyone. It comes with a GNU General Public Licence which gives the end user the permission to use and modify it as and when required.
SAS enables the analysis of complex and large data sets extremely efficiently, and can be used for almost any kind of statistical modelling and analysis. It is also robust enough to maintain its position at the top of the analytics software heap.
R comes with something called packages which allow specialized statistical techniques, graphical devices (ggplot2), import/export capabilities, and reporting tools (knitr, Sweave), etc. Other than its core packages, it has about 7800 additional packages. It has also been getting steadily more poweful with each stable release. Corporate acceptance of R has never been higher.
The past five years have seen a 50-40 split, respectively, in market share between SAS and R. R is pretty much neck to neck with SAS in the current day market scenario!
And the winner is…..YOU!
But what does all this mean to someone who is beginning, or looking to begin, a successful career in analytics? Simply that it is very beneficial and well-advised for data professionals to know both these softwares in order to excel in the field. Salary and pay reports in recent years suggest that professionals who have sufficient experience working with SAS and R, earn a sizeable pay package of 8.5 LPA and 10.2 LPA on average, respectively. The reports also indicated that someone with both SAS & R skills can make upwards of 12 LPA. Other than the obvious pay benefits, the other major thing that factors here is the fact that companies (such as Genpact, Accenture, Infosys to name a few) now are using both SAS and R based on company and client needs. This calls for them to look for people skilled in both of these. Need we say more?
What to do? We’ll tell you!
If your curiosity has been piqued and you wish to learn more about these two tools, check out our comprehensive Data Science Specialization course, where you will learn all about SAS and R.