Having nearly completed Stanford Professor Jennifer Widom's Introduction to Databases, I've recently begun two more massive open online courses (MOOC's) that you, the reader, might want to take a look at, both of them offered by Coursera.
The first course is Social Network Analysis, taught by Lada Adamic of the University of Michigan. This methodology, which can be applied to topics as divergent as infrastructure and epedemiology (as well as the more obvious targets such as Facebook), obviously plays a prominent role in data science, which is one reason to take the course. A second reason is that the course features an optional programming track with four assignments (including a peer-graded final project), some using NetLogo and some using R, and in my case I'm taking the course in part as a way to learn R. The course also makes use of Gephi for basic network analysis. In the second week, there are two versions of the lectures, with an advanced version for students with a background in probability distributions and differential equations; it's not clear if this will be the case in later weeks. This is a nine-week class, and if you're reading this soon after I've posted it, you can still sign up and get full credit, since the first assignment isn't due until Friday night (March 15th).
Taking the advice of one of my contacts to learn something about business, I've also signed up for a non-technical course, Foundations of Business Strategy, taught by Michael J. Lenox of the Unviersity of Virginia. This six-week class features a textbook that Lenox is currently developing, as well as the case method typical of business-school education (Lenox recommends small-group discussion to get the full impact of this method). The most interesting feature of the course is a peer-graded final assignment in which each student writes a short but well-researched strategy memo for a the CEO of a company of his or her choice; more interesting still, Lenox has invited organizations that would like their strategy assessed to join the course and offer themselves as cases for the students' final projecdts. Though we're already about 25% of the way through the course, the assignments all have the same deadline of April 14th, and so it's easy to catch up.
You might also keep a lookout for two courses starting the latter half of April, An Introduction to Interactive Programming in Python, taught by a team from Rice University, and the perennially popular Machine Learning, taught by Coursera co-founder Andrew Ng of Stanford (this one uses Octave, a close relative of MATLAB, for those keeping track of programming languages).
I think that's certainly true, but you don't need a whole degree program in order to benefit from online learning. None of the courses I mention above provides credit toward a degree, but all of them impart valuable knowledge and skills. Indeed, some of us don't have the time or money that earning a whole new degree would require (and, with four degrees already, I'm not sure I need another one :).
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