cool, thanks, just pm'd him. Spunky, what specifically are you doing your Phd in? What is your dissertation subject? How rigorous does Phd Statistics get compared to Phd in Math, is there a lot of pure proof work or its a lot more computational?
I just finished a course on it at coursea and while there, there were discussions with business folks and the CEO of kaggle. The general consensus was:
1. The thing about so called "big data" is that there really is no official training programs so to speak. What is more important is to be able to show what you can do. Good results in kaggle and other comps can lead directly to jobs
2. You spend near on 90% of your time on data cleaning (data munging) so a solid basis in SQL is more important than knowledge of R etc.
3. Being a good programmer is far more important than being a good statistician (see point 2 for reason). Python is a great language to get started with (learn comprehensions!!!!) but R can be a good choice to.
4. Learn how to build random forest models
5. Cannot hurt to learn some map reduce etc. you can have a play http://jsmapreduce.com/