Microsoft R

R is the world’s most powerful, and preferred, programming language for statistical computing, machine learning, and graphics, and is supported by a thriving global community of users, developers, and contributors.The Microsoft R product family includes: Microsoft R Server, Microsoft R Client, Microsoft R Open, SQL Server R Services.Microsoft R Server is the most broadly deployable enterprise-class analytics platform for R . Supporting a variety of big data statistics, predictive modeling and machine learning capabilities, R Server supports the full range of analytics exploration, analysis, visualization and modeling based on open source R. Microsoft R Client is a free, community supported, data science tool for high performance analytics. R Client is built on top of Microsoft R Open so you can use any open source R package to build your analytics. Additionally, R Client introduces the powerful ScaleR technology and its proprietary functions to benefit from parallelization and remote computing.

Microsoft R Open is the enhanced distribution of R from Microsoft Corporation. It is a complete open source platform for statistical analysis and data science. Being based on the open source R engine makes Microsoft R Open fully compatibility with all R packages, scripts and applications that work with that version of R. Microsoft R Open delivers performance boosts, in comparison to the standard R distribution, since R Open leverages high-performance, multi-threaded math libraries.

SQL Server R Services provides a platform for developing intelligent applications that uncover new insights. You can use the rich and powerful R language and the many open source packages to create models and generate predictions using your SQL Server data.

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More Information on Predictive Analysis Process

Predictive Analytics Process Flow

For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.