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Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems where the core input is data. Troves of raw information, are streaming in and stored in enterprise data warehouses. There is much to learn by mining it. Advanced capabilities can be built with it. Data science is ultimately about using this data in creative ways to generate business value.

R is a language which is widely used for statistical analysis, visualization and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team.

The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. R allows integration with the procedures written in the C, C++, .Net, Python or FORTRAN languages for efficiency.

Features :

  • well developed, well structured, simple and very effective, have all the features like other languages conditions, loops, functions input-output facilities.
  • R has effective data handling and storage.
  • R provides a suite of operators for calculations on arrays, lists, vectors and matrices.
  • R provides a large, coherent and integrated collection of tools for data analysis.
  • R provides graphical facilities for data analysis and display either directly at the computer.

As a conclusion, R is world’s most widely used statistics programming language. It’s the # 1 choice of data scientists and supported by a vibrant and talented community of contributors. R is taught in universities and deployed in mission critical business applications. This tutorial will teach you R programming along with suitable examples in simple and easy steps.


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