New Online R Courses from Statistics.com

Online training provider Statistics.com has introduced a couple of new R-related courses which are well worth checking out. These are all self-paced on-line courses, with materials by and interactive feedback from leading R gurus. Current R users looking to take their programming skills to the next level will be particularly interested in the Advanced Programming in R course from Hadley Wickham. Hadley has shared a cracking set of course materials for Advanced Programming in R, so you can see what’s covered.

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The new training courses from Statistics.com are:

Advanced Programming in R – This course will help participants write better code, focused on the mantra of “do not repeat yourself”. They will learn powerful new tools of abstraction, allowing to solve a wider range of problems with fewer lines of code. To get the most out of this course, students should have some experience programming in R already, be familiar with writing functions, and the basic data structures of R (vectors, matrices, arrays, lists and data frames). Participants will find the course particularly useful if they are an experienced R user looking to take the next step, or moving to R from other programming languages and want to quickly get up to speed with R’s unique features.

Statistical Analysis of Microarray Data with R – This course will acquaint you with the process of microarray data mining from beginning to end. You will learn how to how to preprocess the data, estimate gene expression patterns, cluster genes to detect interesting gene expression patterns, and classify experiments (subjects) based on gene expression patterns. Illustrations of the statistical issues involved at the various stages of the analysis will use real data sets from DNA microarray experiments.

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And here’s the calendar of their R-related courses for the remainder of the year:

5/27 – 6/24 Biostatistics in R: Clinical Trial Applications

6/17 – 7/15 Data Mining in R

6/24 – 7/22 Advanced Programming in R

7/22 – 8/19 Visualization in R with ggplot2

8/19 – 9/15 Modeling in R

9/02 – 9/30 Introduction to R – Data Handling

10/28 – 11/25 Programming in R

10/21 – 11/25 Statistical Analysis of Microarray Data with R

11/18 – 12/16 Introduction to Support Vector Machines in R