This list is a little different than the others. Most of this site is focused on R materials that you can consume quickly. The links below are for online coursework to help further your understanding of statistical programming. If you have the time to devote to regular study of this material, you'll definitely be glad that you did.
If this all looks too advanced, take a look at the free statistics courses.
One thing to note is that these are not degree seeking programs. Some of them provide a certificate upon successful completion, but you won't get college credit. Don't let that dissuade you though, this is excellent instruction free for the taking.
Computing for Data Analysis at Coursera - This one starts in September 2013. I took it last fall and really enjoyed it. The material is well presented and easy to follow, and I like Dr. Roger Peng's style. Coursera courses are fantastic, easy to follow and feature quality material from reputable schools. Also, once the course is complete you'll have the option of downloading a certificate of completion. Begins in September of 2013.
Try R at Code School - This is a free, online, self-paced course covering introductory concepts in R programming and simple data analysis. It is a joint effort between Code School and O'Reilly Media. The course has seven chapters, each with its own quiz to ensure understanding. If you're new to R, this is a fantastic place to start.
Algorithms: Crunching Social Networks at Udacity - This is a self-paced course taught by Dr. Michael Littman. This course is also new, so no review. Udacity has a reputation for quality instruction and their courses are broken up into short, easy to follow videos. There are short quizzes and weekly assignments. Note that you're likely to work with Python in this course, but there's a pretty strong connection between Python & R programming, so it'll absolutely be a benefit.
Introduction to Data Science at Coursera - This is another Coursera course and is a little more than your standard R course. It does cover statistical analysis & data visualization, but it also goes into relational algebra, SQL, mapReduce, machine learning algorithms & more. If you're really interested in data analysis & data science, then you want this material. The course started in May of 2013 so is almost complete at this point, but the video lectures & course forums should remain available for a few months.
Computer Science at Khan Academy - This is a collection of instructional videos presented by Salman Khan, which means that you can complete them at your own pace. This isn't a course in the strict sense, its a collection of videos on scientific programming with Python. Even so, this is valuable information for anyone wanting to further their knowledge of statistical programming with R. Khan Academy allows you to go at your own pace, and the instruction is quite good.
Statistical Computing by Carnegie Mellon University - This self-paced course is a little different in that it doesn't include instructional videos. It's included here because it's directly on point. As the title states, this is on statistical computing, and most of the coursework is completed in R. Following material like this will require a bit more discipline on your part, but you'll be rewarded with skill and confidence in working with R.
Statistical Programming Tutorial Videos by sentimentmining.net - This is a great set of 28 instructional videos that introduce basic R concepts as well as clustering and neural networks. View them at your pace, or in any order. The videos are well done, easy to use and move at a comfortable pace. This is a nice resource. I'm unsure how much longer it will be aronud. It looks like the domain isn't being maintained, so this could disappear at some point.