Open Source

Enterprise Scale Analytics with R – New White Paper

As R has become more popular, the role of analytics has become increasingly important to organizations of every size. Increasingly, the focus is on enterprise scale analytics—using advanced, predictive analytics to improve every decision across the organization. Enterprise-scale adoption of analytics requires a clear sense of analytic objectives; an ability to explore and understand very […]

Challenges Scaling Open Source R – Part 2

I recently did some research on the requirements for enterprise-scale analytics and the challenges of using open source R in this context. In my first post (Requirements for Enterprise Scale Analytics with R – Part 1) I outline some of the requirements I see for enterprise scale analytics. In this second post I will discuss the challenges of R […]

Requirements for Enterprise Scale Analytics with R – Part 1

I recently did some research on the requirements for enterprise-scale analytics and the challenges of using open source R in this context. In this first post I wanted to outline some of the requirements I see for enterprise scale analytics and in a second post I will discuss the challenges of R in that context. As advanced […]

Standards in Predictive Analytics: R

The third post in my series on standards in Predictive Analytics is on R, a hot topic in analytic circles these days. R is fundamentally an interpreted language for statistical computing and for the graphical display of results associated with these statistics. Highly extensible, it is available as free and open source software. The core environment provides standard programming capabilities as well as specialized capabilities for data ingestion, data handling, mathematical analysis and visualization. The core contains support for linear and generalized linear models, nonlinear regression, time series, clustering, smoothing and more. The language has been in development and use since 1997 with the 1.0 release coming in 2000. The core is now at release 3.0. New capabilities can be added by creating packages typically written in the R language itself. Over 5,000 packages have been added through the open source community.

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