by Arlene Minkiewicz
| October 26, 2015
Original Post date: September 30, 2015
I recently read an interesting article in IEEE Spectrum “The 2015 Top Ten Programming Languages” (http://spectrum.ieee.org/computing/software/the-2015-top-ten-programming-languages). The article fairly states that popularity of a programming language is clearly subjective – depending on what the goals of the end user are but they tried to adjust their weightings based on their perception of the interests of the members of IEEE (Institute of Electrical and Electronics Engineers). Many of the results of their ranking were expected and not particularly interesting. Java, C, C++, C# and Python continue to be the top 5 programming languages that developers are using and employers are looking for.
But there seems a bit of a surprising trend with number 6. R, which is a statistical computing programming environment has advanced from 9th in 2014 to number 6 in 2015. R is a language that is used by data analytics professionals to facilitate big data analysis. It is available as an open source application that is freely available under the GNU General Public License. It is an interpretive language that uses a command line interface that, while not completely intuitive, is quite powerful. Click here to learn more about R. (https://en.wikipedia.org/wiki/R_(programming_language)
The trend toward big data analysis is not surprising. Significant improvements in hardware and software technology over the last 10 to 15 years have resulting in computing power capable of performing analysis on amazingly large quantities of data. ‘Big data’ is a buzzword for the technology and methodologies that make it possible to analyze gigantic quantities of heterogeneous sets of data. We find examples of big data applications in our everyday lives. Simple consumer examples include how Amazon knows what purchases you might like and Facebook know who to suggest you might like to be ‘Friends’ with. Obviously there are huge potential advantages to retailers if they can use data about your previous buying habits and even your behavior in their stores (or their website) to find ways to influence you to purchase more merchandise.