Statistical R Programming in Business Intelligence

Business challenges are significantly impacting & enabling technologies to use the statistical methodologies in business intelligence helping IT teams to build the better & faster analytical insights with the readily available mathematical & statistical functions as packages with the ease of easy to manage the packages & project scripts for Administrators. With the recent trend in the industry companies are adapting Digitization & AI mechanisms to automate their IT workloads by drastically reducing the IT teams & application systems, IT Infrastructure to optimize their investments & cost by utilizing the free wares & open source programming languages. 

At the same time capacities of the computing is essentially growing, querying the data scattered around distributed network is more essential & this needs powerful programming languages like ‘R’. For example Blockchain with distributed ledger network (DLN) is expected outperform the existing unconnected network especially in the banking domain “core banking” that’s what we used to call which is an alliance network between the banks to complete transactions or instant money transfers but block chain is going to be 100 times faster than this legacy system & it allows the completion of transactions within a fraction of seconds.

R basically provides below set of functions & as well as more advanced functions.

1) Mathematical Functions
2) Graphical Functions
3) Statistical Functions
4) Data Management Functions (to Sort, Merge, Aggregate)
5) User Defined Functions (Let’s programmers to define their own functions).

At this point in time R is widely used in healthcare & BFSI domain as the statistical calculations are much needed in these domains. Developers can call the R functions which are stored as objects in R libraries & use the predefined functions to do the data calculations & plot the graphs to display the business intelligence reports in an HTML format. 

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