Academic

Harvard's quantitative social scientists appear to spend most of their time devising ingenious R and Stata plug-ins.  Which, don't get me wrong, is really fun.  But sometimes they devote the quantitative workshop to stuff like (this week, in this case) this:

Amanda Cox (who is with the New York Times) ... will present "Open Problems in NYT Graphics". Amanda provided the following abstract:

The New York Times graphics department is a group of about 30 journalists who make the charts, maps and diagrams for the print and online versions of the paper. This talk is a (completely unofficial) guide to some of the problems the department faces on an ongoing basis, including how to represent uncertainty in an accessible way, and how to move beyond something I call "Here is some data:" toward something closer to inference.

Should Wharton's people regret their obsession with finding the new largest prime number?  I think not.  Here's last week's talk at the place where business meets statistics:

In October 2006, the DVD rental company Netflix released more than 100 million user ratings of movies for a competition to predict users? ratings based on prior ratings. One allure to data analysts around the world was a $1,000,000 prize to the first team to reach a ten percent reduction in root mean squared prediction error relative to Netflix? current algorithm (our team currently leads with a tantalizing 9.44 percent improvement). However, the modeling challenges and data provide an even larger attraction for many competitors. Due to the size of the data (over 17,000 movies and 480,000 users), the competition has mainly attracted computer scientists; however, there are many modeling challenges (e.g., over fitting) that can benefit from the perspective of statisticians. After describing some of the techniques in use and advances spurred by the competition, I will speculate on some broader lessons of interest to statisticians.