Data analytics made easy with R programming

Individuals who have an interest in both data analysis and computing can enhance their knowledge by learning the R language. The R programming language can be used for graphics and statistical computing. It helps companies get an insight into complex data. This interactive object-oriented language has been designed by statisticians for statisticians. To understand this language best, one should enrol into an R programming training course.

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Functions for almost every statistical model, data manipulation or chart required by data analysts can be created with this language. By virtue of being open source, its source code is easily available for modification and use for someone who wants to see how its algorithms work underneath the covers. It has benefited from the development of other successful open source projects such as MySQL and Linux, since code has been vastly improved over the years.

Advantages of R   

As mentioned above, it is open source and thereby available for free. It is powerful and also highly extensible in nature. Being an environment that makes use of command line scripting, a number of complex data analysis steps can be stored within R. This helps one in reusing the previous analysis work on similar data easily, instead of relying on a point-and-click interface.

It is easier for others to validate one’s research results and check work for possible errors. Interestingly, an Excel coding error was previously identified during 2013 in a reputed economic analysis report called Reinhart/Rogoff. R may be difficult to learn in the beginning, but it is immensely helpful in the long run.

Various programming features of R

Like other languages such as MATLAB and APL, R also supports the use of Matrix Arithmetic. Its data structures consist of arrays, data frames, vectors, matrices and data lists. R also supports procedural programming through functions and object-oriented programming through generic functions.

R is commonly used as an environment for software development and statistical computations. However, it can also work as a generic matrix calculation toolbox, which has performance benchmarks close to MATLAB. Such factors have helped it become one of the leading languages for data scientists.

Being taught by experienced instructors

R is a highly technical language, which means that it requires the expertise of an instructor. This instructor must be trained in teaching the practical aspects of the programming language to the aspirants. He or she must understand how to explain the concepts to aspirants in a simple manner. Appropriate guidance must also be provided during lab sessions. Any reputed training institute will certainly have experienced instructors.

Therefore it is clear that R programming language is very helpful for statisticians and data scientists. People like academicians, PhD scholars, consultants, risk analysts and survey researchers can make good use of the language. For more information, one can take up an advanced business analytics training course.

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