R Programming for Data Science | A Complete Online Courses to Learn

Learn R Programming for Data Science Online

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What is R programming?

R is a powerful language and environment that is used for statistical computing and graphics. Very similar to the S language and environment that was developed at Bell Laboratories by John Chambers and colleagues, while the R programme was a part of the GNU project. R can be considered as a different implementation of S and there are some important differences while so much of their code is similar for both S and R.

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Learn R Programming for Data Science OnlineWhat is R programming?

R is a powerful language and environment that is used for statistical computing and graphics. Very similar to the S language and environment that was developed at Bell Laboratories by John Chambers and colleagues, while the R programme was a part of the GNU project. R can be considered as a different implementation of S and there are some important differences while so much of their code is similar for both S and R.

R provides a lot of variety for statistical including linear and non linear modeling, classical statical tests among others as well as for graphical technique which is quite extensible. The S language is, therefore, a great choice for research in statistical method and R provides an Open source to match the requirements.

One of the biggest strengths of R is that it is easy to integrate it with publication quality plots including symbols and formulas related to mathematical equations. So while great care has been taken to ensure default choices in minor design changes, the user is more often than not in full control of the software. In addition, R is available as a free software under the terms and conditions of Free Software Foundation’s GNU General Public License. It is capable of being compiled and run on a number of platforms including UNIX platforms, MacOS and Windows.

An integrated suite for software facilities that are used for data manipulation, calculation, and graphical display, it has the following features:

1. A good data handling and storage capability
2. A suite of operators that can calculate a variety of matrices and arrays
3. A large, coherent and integrated collection of tools that can effectively help companies to analyse data
4. Graphical facilities for data analysis and displays which can be availed either on the screen or through hard copies
5. A well developed simple and effective programming language that includes loops, conditionals, and user-defined recursive functions.

In short, R is a programming language and software environment that is used for statistical computing and graphics. The R language is further mostly used by statisticians and data miners so that they can develop new and innovative statistical software and data analysis methods. Polls, surveys of data miner and studies have effectively proved that the popularity of R software has increased substantially in the recent years.

R Programming for Data Science Course Description

1. Overview and History of R: Here you will learn about how R as a software evolved and grew in the industry
2. Datatypes and Basic Operations: Here you will learn about data types and how to operate them in an effective fashion
3. Reading Data: This section will help you understand how to read data in an effective fashion
4. Scoping Rules: In this section, you will learn the importance of scoping and its related rules

R Programming for Data Science ideal target audience for this course would include individuals who are trying to get into analytics domains. Graduates who are trying to discover a new career field and are passionate about working in an exciting industry is also perfect for this course. In addition, anyone who wants to learn about R programming would be an effective target audience for this course as well.

1. If you are interested in being a part of this course, it is important that you have at least a basic understanding of how a computer system operates
2. You must have a basic understanding of basic programming concepts
3. You must have at least some knowledge about statistical reasoning
4. Being a passionate learner will help you learn more about the industry in an effective manner

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