**Revolutions**, and kindly contributed to R-bloggers)

If you're in San Francisco for this week's DeveloperWeek conference, our own Joe Rickert will also giving a presentation on Wednesday at 2:10PM on Predictive Modeling with Big Data in R which will feature several demos of data mining massive data sets using the Revolution R Enterprise. Incidentally, the whole team Revolution Analytics was proud to receive the Top Innovator award for Big Data Technologies from DeveloperWeek.

If you can't make it to DeveloperWeek, Joe will be also giving a free one-hour webinar on February 14, Introduction to R for Data Mining. If you're new to R and have been thinking about learning the R language, this is a great starting point. I've included the full webinar description below, and you can registering here to secure your seat and to be notified when the replay is available.

**Introduction to R for Data Mining**

Date: |
Thursday, February 14, 2013 |

Time: | 10:00am – 11:00am Pacific Time (Click here to see the webinar time in multiple time zones) |

Presenter: |
Joseph Rickert, Technical Marketing Manager, Revolution Analytics |

We at Revolution Analytics are often asked “What is the best way to learn R?” While acknowledging that there may be as many effective learning styles as there are people we have identified three factors that greatly facilitate learning R. For a quick start:

- Find a way of orienting yourself in the open source R world
- Have a definite application area in mind
- Set an initial goal of doing something useful and then build on it

In this webinar, we focus on data mining as the application area and show how anyone with just a basic knowledge of elementary data mining techniques can become immediately productive in R. We will:

- Provide an orientation to R’s data mining resources
- Show how to use the "point and click" open source data mining GUI, rattle, to perform the basic data mining functions of exploring and visualizing data, building classification models on training data sets, and using these models to classify new data.
- Show the simple R commands to accomplish these same tasks without the GUI
- Demonstrate how to build on these fundamental skills to gain further competence in R
- Move away from using small test data sets and show with the same level of skill one could analyze some fairly large data sets with RevoScaleR

Data scientists and analysts using other statistical software as well as students who are new to data mining should come away with a plan for getting started with R.

Revolution Analytics webinars: Introduction to R for Data Mining

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