# Webinar: High-Performance Analytics with R and Microsoft HPC Server

**Revolutions**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

On April 14 I’ll be giving a new webinar in partnership with Microsoft on High-Performance Computing with R. I’ll be focusing on the new parallel programming capabilities of REvolution R Enterprise 3.1 for Windows, and how to use the features of Microsoft HPC Server to enable computing on clusters. Here’s the complete agenda, and you can register at the link below.

Statistical data analysis is a key part of the operations of just about every business today. But as data sets get larger, analyzing trends or generating predictions becomes more and more of a challenge.

If you’re doing predictive modeling today and find that you can no longer use all of your data because of size limitations, or the computations are taking too long for you to take action on the results, then the parallel-processing capabilities of Windows HPC Server can help. In this webinar, we’ll introduce the R language for statistical computing, and show how the easy-to-use parallel programming capabilities of REvolution R Enterprise work with a HPC cluster to cut processing times by an order of magnitude or more.

We’ll give practical examples of how to speed up many kinds of analytic computations, from simple summary statistics to cutting-edge tools like ensemble predictive models.

Audience: Programmers, researchers and analysts who need to process large volumes of data for data mining, statistical analysis, or predictive analytics

REvolution events: High-Performance Analytics with REvolution R and Windows HPC Server

**leave a comment**for the author, please follow the link and comment on their blog:

**Revolutions**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.