Why R Programming for statistical analysis and statistical computing?

Of late, R programming language has become preferred programming platform among many professionals who are into data analysis and forecasting. The reason being R programming supports statistical analysis; statistical computing, and graphical data representation. These include classical statistical tests, linear and nonlinear modelling clustering, etc. In fact, the support for multiple analysis features helps in detailed analysis of data and exact forecasting. Also it contains its LaTeX-like documentation format, used for documentation.

What separates R programming language from the rest?

With R programming libraries, it becomes possible to carry out a wide range of statistical and graphical computing tasks. The extensive libraries allows to work on time-series analysis; linear and nonlinear modeling, clustering; classical statistical tests, and many others. R comprises of functions and extensions which allow it to be extensible. Furthermore, since it entails standard functions coded in R programming language, it becomes simple for users to understand and select one from the available algorithmic choices. Besides, the software has its own community where there are active contributions in form of packages. Around eight packages are offered with the R distribution and more at CRAN family of websites, consisting of a host of modern statistics.

Salient features of R programming language

R, being Open Source software compiles and runs on a host of operating systems such as UNIX; FreeBSD; Linux; Windows, and MacOS. It offers a wide range of features for data analysis; data calculation; data manipulation, and graphical representation. Some of its distinguishing facilities are:

• Data management facility
• Big, integrated, and lucid set of tools for data analysis
• Data storage facility
• Tools for working on arrays in certain matrices
• Graphical representation for analysis of data
• Facility to view graphs on-screen or hardcopy
• Offers user-defined recursive functions; loops; input and output features, and conditionals.
• Provides features to include mathematical symbols and formulae wherever required on publication plots

R Programming Training

To get a complete understanding of R, one can enroll for R Programming Training. To identify the institutes which offer the training, one can check for R Programming Training on internet. For example, keyword such as R Programming Training in Noida can be googled.

The training will familiarize aspirants with the basics of R programming and concepts required for performing statistical analysis and computing. These includes writing R function, reading data into R, accessing R packages, debugging; data exploration with histograms, box plots, correlation coefficients among others. In addition during R Programming Training, aspirants will get acquainted with:

1. R libraries functions
2. Libraries for statistical and graphical analysis
3. Classical statistical tests and clustering
4. Regression models
5. Simulation and numerical optimization
6. Data frames and time-series analysis
7. Using R for distributions, probability, correlation and regression
8. Advance data handling techniques
9. Programming statistical graphics
10. Subscripting; reshaping of data, and character manipulation.
11. Sample size and its calculation

read more visit:http://www.multisoftsystems.com/business-analytics/r-programming-training

The author is an IT professional at Multisoft Systems having years of experience in the IT industry.