Revolution Analytics Fall Webinar Series

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We’ve lined up what we think is an amazing series of R-related webinars over the next couple of months. These free 30-60 minute webinars will cover a wide range of topics: big-data analysis in R with the RevoScaleR packageHadoop and Netezza; introductions to R for SAS users and for R users new to Revolution R; and applications of R in Finance for analyzing mortgages and the 2010 Flash Crash. The full details of the webinar series is after the jump, and you can also subscribe to our Updates email list for timely notifications of these and other upcoming webinars in the series.

Wed, Sept 21th
10AM – 10:30AM Pacific Time

Leveraging R in Hadoop Environments

Traditional IT infrastructure is simply unable to meet the demands of the new “Big Data Analytics” landscape.   Many enterprises are turning to the “R” statistical programming language and Hadoop (both open source projects) as a potential solution. This webinar will introduce the statistical capabilities of R within the Hadoop ecosystem.  We’ll cover:

  • An introduction to new packages developed by Revolution Analytics to facilitate interaction with the data stores HDFS and HBase so that they can be leveraged from the R environment
  • An overview of how to write Map Reduce jobs in R using Hadoop
  • Special considerations that need to be made when working with R and Hadoop. 

We’ll also provide additional resources that are available to people interested in integrating R and Hadoop.

Wed, Sept 29th
11AM – 12PM Pacific Time

Netezza vs. the Cloud: Using R to Profile Distributed Computing Platforms with Applications in Backtesting FINRA’s Limit Up/Down Rules

This webcast is for statisticians, analysts and IT teams responsible for Big Data Analytics who are looking for ways to achieve greater innovation and leapfrog current performance. On May 6, 2010, at 2:45 PM, the Dow Jones Industrial Average plummeted approximately 900 points and rebounded within a matter of minutes. This temporary disappearance of one trillion dollars in market value prompted hearings by the U.S. Coangressional House Subcommittee on Capital Markets, Insurance, and Government Sponsored Enterprises to investigate this event, which later became known as Flash Crash. As a result of these hearings, the Financial Industry Regulatory Authority (FINRA) instituted rules to regulate trading in the event of a precipitous drop in stock price.

Wed, Oct 5th
11AM – 12PM Pacific Time

An Introduction to R for SAS and SPSS Users

R is free software for data analysis and graphics that is similar to SAS and SPSS. Two million people are part of the R Open Source Community.  Its use is growing very rapidly and Revolution Analytics distributes a commercial version of R that adds capabilities that are not available in the Open Source version. This 60-minute webinar is for people who are familiar with SAS or SPSS who want to know how R can strengthen their analytics strategy.

Thurs, Oct 13th
1PM – 2PM Pacific Time

Successful Uses of R (along with SAS and Excel) in Banking:  A Look at Mortgages, Loan Portfolios and Probabilities of Default

Hong Ooi’s analysis supports bottom line-impacting decisions made a wide spectrum of groups at Australia and New Zealand Banking Group (ANZ). He has broad experience with both SAS and R, and depends on R for the bulk of his analysis. In this webinar, he will discuss his challenges and how he’s using R along with SAS and Excel to overcome them.

Wed, Oct 19th
10AM – 10:30AM Pacific Time

Revolution R Enterprise – 100% R and More

R users already know why the R language is the lingua franca of statisticians today: because it’s the most powerful statistical language in the world. Revolution Analytics builds on the power of open source R, and adds performance, productivity and integration features to create Revolution R Enterprise. In thiswebinar, author and blogger David Smith will introduce the additional capabilities of Revolution R Enterprise.

Wed, Oct 26th
10AM – 11AM Pacific Time

Scalable Data Analysis in R

For the past several decades the rising tide of technology — especially the increasing speed of single processors — has allowed the same data analysis code to run faster and on bigger data sets. That happy era is ending. The size of data sets is increasing much more rapidly than the speed of single cores, of I/O, and of RAM. To deal with this, we need software that can use multiple cores, multiple hard drives, and multiple computers. That is, we need scalable data analysis software.

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