Online Course Reviews: Coursera's Social Network Analysis and Foundations of Business Strategy—Plus New Courses to Check Out

I've recently completed Foundations of Business Strategy, taught by the University of Virginia's Michael J. Lenox, and I've submitted the final project for Social Network Analysis, taught by the University of Michigan's Lada Adamic, and I'd like to share some comments on both of these oferrings from Coursera, as well as give readers a heads-up to other courses that have just started.

Social Network Analysis provided a good survey of the methods and applications in the field, covering random networks, measures of centrality, small world networks (and other topics related to the question of optimization), and the dynamic aspects of networks, such as contagion and opinion formation. Adamic's explanations were usually clear, and even a student with little knowledge of probability could have gotten the gist of most of the course material (and made use of Gephi to perform basic analysis), but equations were presented for those who wanted them, and the readings gave further detail. In fact, this is the only course I've had so far that made extensive use of academic journal articles (and a few written for a wider audience), some of them required and some recommended—they gave a much better impression of the history of social network analysis and the current state of the art than Professor Adamic could have given by herself. From a personal perspective, this topic particularly interests me because I can see how social network analysis might be applied to the study of ethnic politics, my previous area of research.

The course's only weakness lay in the (optional) programming track: the first three programming assignments, two in R and one in NetLogo, were largely exercises in copy-and-paste, rather than posing full-fledged coding tasks; they were, however, enough to give students basic familiarity with the two programming languages, and with the igraph package for R. In contrast to these "canned" assignments, the final project was almost completely unstructured, and while this provided welcome freedom to explore whatever topic a student wished, it also meant a steep learning curve for someone whose experience with R or NetLogo was limited to the earlier exercises.

Compared to the other courses I've taken, Foundations of Business Strategy proved much less time-consuming, with required readings limited to very short chapters from a forthcoming book by Lenox (and when I say "short", I mean it's more a pamphlet than a book, with chapters only a few pages long), and a business case each week. The professor encouraged discussion of each case, both in small groups and in the discussion forum, and each week recorded a debriefing that made reference to students' comments in the forum; however, the only assignments that needed to ber turned in before the final project were (relatively easy) weekly quizzes that covered the lecture topics. Quizzes, hence the lectures the quizzes covered, could be completed at any time during the course, though completing the lectures late meant that a student had no chance to participate in the discussios of the associated cases. The final project was a 1500-word "executive summary" of a strategic analysis of an organization of the each student's choice.

Despite the sparseness of the course material, the class provided a useful framework for business strategy, a framework—and this is the part that surprised and impressed me, after all the scurrilous rumors I've heard about business schools, and the weak business students I've taught in my own classes—that was solidly grounded in microeconomics, with no mention at all made of the latest management fads. No, someone who took this course isn't guaranteed to become a strategic genius, or even, necessarily, an effective strategic thinker, but that's because strategy requires making decisions in an environment that's inherently complex and ambiguous, the upshot of which is that giving students a good framework for organizing thought—and a chance to practice strategic thinking on real cases—is about the best that a teacher can do.

My one serious concern with the course was the rubric used for peer review of the final project: the assignment presented a set of criteria for grading that focused largely on the quality of analysis and writing, and specifically warned against trying to use all the methods of analysis that had been covered in the course; by contrast, the rubric that was actually used for peer grading of the assignment amounted to a checklist of topics in the course, and penalized students for not covering each one, while leaving little room for judging the quality of the report. As a former professor, I certainly understand that detailed rubrics, while beloved by students, tend to push attention in grading towards mechanical aspects of the assignment, and this probably goes double for peer assessment, but it is possible to create rubrics that give more or less clear guidance for making qualitative judgments. More importantly, whatever rubric is used needs to match the criteria that are spelled out in the original assignment.

New Courses to Check Out

I've signed up for three courses that have just started, though odds are I'll need to withdraw from one of them, due to time constraints. I stumbled upon Probabilistic Graphical Models—the term "graphical" didn't suggest anything I was interested in, but a look at the course description revealed that probabalistic graphical models (PGM's) includes Bayesian and Markov networks, both of which feature in decision-making and machine learning, and both of which show up repatedly in job ads for data scientists. Coursera co-founder Daphne Koller, of Stanford University, lacks the charisma and clear explanations of the three MOOC professors I've previously learned from; she also comes off (if I may be subjective here) as a bit pretentious, an impression that makes her inclusion of the Simpsons' family tree as an example of a genealogical network more cringeworthy than cool. I've found most of the material so far to be readily understandable, but I'm guessing it wouldn't be for someone without my background in statistics (especially since I've used by structural equation and time series models in my research, and these feature many of the same concepts found in PGM's). This is a graduate-level course, and Koller herself describes it as challenging even by those standards. Needless to say, the other side of that coin is that anyone who gets through the course will have a solid foundation in PGM's, and also, for those taking the programming track, knowledge of Octave and/or MATLAB (the two are close relatives), especially given weekly programming assignments in a 11-week course.

I've also just started An Introduction to Interactive Programming in Python, taught by multiple instructors from Rice University, and Machine Learning, this iteration taught by Coursera co-founder Andrew Ng of Stanford, one of two professors on Coursera to teach this course; like Probabilistic Graphical Models, Machine Learning makes use of Octave. I doubt however that I'll have time for all three courses, meaning that I'll likely have to withdraw from one of them, which will probably be Probabilistic Graphical Models or Machine Learning, given their overlap in programming language, and, to a lesser extent, subject matter.