Guest post: Statistics.com is offering a list of courses about R from some of the leading experts in their fields. They offer a $200 discount for anyone who orders any 3 courses from them at one time. Here is the list of R specific courses (with links):

Feb 10: Introduction to R – Data Handling (http://bit.ly/z9k0vk) taught by Paul Murrell (“Introduction to Data Technologies,” member R core development team)

Mar 9: Modeling in R (http://bit.ly/yZejUE) taught by Sudha Purohit (“Statistics Using R”)

Apr 13: Programming in R (http://bit.ly/wrUIuj) taught by Hadley Wickham (“ggplot2: Elegant Graphics for Data Analysis,” and the R packages ggplot and reshape R)

May 18: Introduction to R – Statistical Analysis (http://bit.ly/xNH4tw) taught by John Verzani (“Using R for Introductory Statistics”)

Jun 15: R Programming Advanced (http://bit.ly/zqgz4e) taught by Hadley Wickham (“ggplot2: Elegant Graphics for Data Analysis,” and the R packages ggplot and reshape R)

The following are domain- and technique-specific R courses:

Mar 23: Survey Analysis in R (http://bit.ly/ynhXie) taught by Thomas Lumley (“Biostatistics: A Methodology for the Health Sciences” and the R survey package)

Apr 20: Statistical Analysis of Microarray Data with R (http://bit.ly/yn206U) taught by Sudha Purohit (“Statistics Using R”)

May 11: Graphics in R (http://bit.ly/zLC5LJ) taught by Paul Murrell (“Introduction to Data Technologies,” member R core development team)

May 25: Biostatistics in R – Clinical Trial Applications (http://bit.ly/woz2Bh) taught by Din Chen and Karl Peace (“Clinical Trial Data Analysis Using R”)

Jun 22: Smoothing with P-splines Using R (http://bit.ly/xv4UaG) taught by Paul Eilers and Bryan Marx

Jun 29: Data Mining in R (http://bit.ly/z0gNVd) taught by Luis Torgo (“Data Mining in R”)

Jul 20: R – ggplot (http://bit.ly/w78JgZ) taught by Hadley Wickham (“ggplot2: Elegant Graphics for Data Analysis,” and the R packages ggplot and reshape R)

Nov 16: Introduction to Support Vector Machines in R http://bit.ly/zRA9dr

TBA: Spatial Analysis Techniques in R http://bit.ly/xBMKl9



