Why every business analyst needs to learn R?
Here are some of my views on why every business analyst needs to learn R. I have worked with SAS and SPSS language products for over 10 years and R language for over 4 years now.
1) Better Job Security with R– R is open-source and free. It saves on cost of software as a capital cost, thus freeing up financial resources for hiring more and more analysts with advanced degrees.
With the paradigm of cloud computing, hardware costs are minimized as well, thus reducing overall cost of statistical computing and making it much more affordable and accessible even to companies that are not used to investing thousands of dollars in annual software licenses, or companies that have an analytical need , but not much of software budget. R thus brings back the focus of analysis back on the analysts rather than the software.
Having R language skills can also make analysts a hero within their analytical department since they can help cut down the annual budget cost of their department. With an uncertain economic environment and job layoffs happening, it makes sense to learn a language that is free to use, in case your organization is facing a crunch it can simply switch to R, rather than letting go or laying off trained and experienced analysts.
With open -source R, you can build in customized applications especially if you want to build proprietary algorithms or software.
This is particularly true for use in banking and financial services.
2) Better Business Acceptability with R – Businesses are moving towards R rapidly . As per the seminal annual survey of data miners, Rexer Analytics , R became the dominant platform in 2010.
Companies as diverse as Google, Pfizer,Merck, Bank of America, the InterContinental Hotels Group and Shell , Microsoft (Bing),Facebook, Llyod’s Insurance, ANZ Bank,NY Times, Thomas Cook are using the R language for their analytical tasks.
Existing Commercial Vendors of R language Products
Many existing companies offer solutions for R.
• Revolution Analytics
• XL Solutions-
• Information Builder –
• Blue Reference- Inference for R
• R for Excel
Software vendors that support or plan to support R
There is broad consensus on using R platform within the fields of statistical computing ana analytics. The range of companies that support R include the SAS Institute that enables working with R through SAS/IML and JMP software , to Oracle that is building it’s
own version of R Enterprise , to Microsoft that has both invested in Revolution Computing(Analytics) and is building solutions for high performance computing with R , to IBM that is using R though it’s acquired companies (Netezza and SPSS ) to SAP ‘s proposed integration of HANA with R, Teradata’s support for R
3) No longer so difficult to learn – R was considered difficult to learn. Now Graphical User Interfaces have evolved. These include packages like Rattle (that is specialized to data mining), Deducer (specialized to data visualization),R Commander ( that allows extensions of other statistical packages through e-plugins and interfaces like R Studio and R Excel that allow very easy usage and adaptability even to newer users and learners in R.
4) Graphs are better in R – R is a good platform to learn data visualization and data exploration through creating graphs and diagrams. This is because of the fact that R’s graphical support is the best compared to any class of analytics software and it includes interactive, 3D , and a wide array of publication ready templates for customizing graphical output. Since analytical results are mostly presented graphically – using R can help explain the statistical solution especially if the audience is a business audience.
5) R has a fast rising pool of students and future analysts– It is a great example of learning object oriented programming as well as statistical thinking, hence R has become the de-facto language to learn within campuses and statistics departments. It is thus easily the platofrm with the greatest availability of analysts in the future.
6) R can handle big datasets– Thanks to advances made with packages like RCPP, big data packages in open source R, and the RevoScaleR package by Revolution Analytics, working with big datasets is just as easy for a trained analyst as it is for any other analytical platform.
I hope this helps to convey my views and satisfy your curiosity on why R is necessary for your analytical career.
( The author is writing a book called “R for Business Analytics” that is expected within 2012)