Mike Driscoll's Dataspora blog covers data science, big data, R, MapReduce and SQL.
HN commenters link its comparison of eight MapReduce languages as entertaining, and its posts on how Google uses R for prototyping.
>Dang, I wish I could find the link to this...an HP data scientist wrote a short essay (something like "Intro to Data Science") and said that the proper collection and cleaning of data is often seen as dirty grudge work that has to be done (by someone else, hopefully) before the real groundbreaking work can be done. However, the author said, this dirty grudge work is the real work.
It's called data munging. Good short article on dataspora about it a while back:
http://www.dataspora.com/2009/05/sexy-data-geeks/
Google apparently runs R on desktops (http://dataspora.com/blog/predictive-analytics-using-r/) and ports it to Python or C++ once their ideas are tested (for production code). Having said that if your list of functions are growing, try packaging up in a library - there are many tutorials available on the web. If your data is becoming big, try using RHIPE (Hadoop integration), bigmemory, snow, or a number of other packages:
http://cran.r-project.org/web/views/HighPerformanceComputing.html
If you guys mostly have CS background you should definitely check this out :
http://www-stat.stanford.edu/~tibs/ElemStatLearn/
It's Data Mining from a statistician point of view. You can download the entire book for free on the website and the graphs and computations are done with R.
This book is also one of Hal Varian's favourites:
http://www.dataspora.com/blog/sexy-data-geeks/
This is an entertaining comparison of 8 different map-reduce languages for Hadoop, including a flame-tastic take on Cascalog/Clojure: http://www.dataspora.com/2011/04/pigs-bees-and-elephants-a-comparison-of-eight-mapreduce-languages/