Review: Coursera Social Network Analysis class

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I recently completed the Coursera Social Network Analysis class. This was my first time taking a Coursera class. In this post, I will describe my experience with Coursera generally, and review the Social Network Analysis class in particular.

Along with several of my colleagues, I took Martin Odersky's Functional Programming Principles in Scala class at the same time. Although I finished my last assignment for that class weeks ago, the class isn't technically complete, so I will reserve comments on that class for a later date.

The Social Network Analysis class is an introductory level class. No previous experience with social network analysis is presumed. The professor is Lada Adamic, of the University of Michigan. If you take the class, you will learn what social network analysis is, the basic mathematical techniques used, a few interesting applications, and have the chance to use software tools such as Gephi, R, NetLogo, and others.

One point which is perhaps not obvious to outsiders is that “social network analysis” is actually relevant to analyzing almost any network which can be modeled as a directed or undirected graph. The class includes lectures on using the techniques presented here on networks which are not actually social networks.

There is quite a bit more math underlying real-world social network analysis then an outsider might expect. Most so-called "social media experts" would probably find it incomprehensible. However, this class really only skims the surface of the math. Students who are not “mathematically inclined” probably won't have any problem getting through the class. Students who really like math will probably want to follow up with something more in-depth.

The class has an optional programming component. You can opt to not complete any of the programming assignments, and still pass the class. However, I think the most valuable things I learned from the class came from completing the programming assignments. It's one thing to hear about, say, a mathematical formula for determining some property of the network, but just reading this formula on the slide sometimes doesn't really stick in my memory. If I actually implement this analysis in R, on the other hand, I have to think about what all of the variables mean. Also, in completing the programming assignments I had the opportunity to use some tools which I had never heard of before, like NetLogo, and other tools, like R, which I had heard about, but never actually used.

If you do complete the programming assignments, then you will receive a “with distinction” on the certificate (a PDF, really) you receive on successful completion of the class. Coursera, however, is not at this time an accredited institution, so you should probably take this class to fulfill your love of learning, rather than because you think it will enhance your resume.

I think the programming assignments would be somewhat difficult for students without prior programming experience. None of them were difficult for me to complete, but somebody without a general sense of common programming syntax, and without real-world experience of setting up slightly-novice-unfriendly tools, might be frustrated by the fairly low level of support included in the course. It is assumed that you are able to install and configure, for example, the R system, pick a text editor and a REPL, install libraries from various locations, figure out differing syntax in various systems, and deal with at least one required library which doesn't always seem to return accurate results. No professional programmer will have a problem with any of this, but I can see several roadblocks to non-programmers. This is unfortunate, because the programming itself is not difficult, and really does help with understanding the material in the class.

Like just about any class I've ever taken anywhere, online or offline, there are “bugs” in the assignments. Judging by forum posts, many people seem to find this surprising, but, in my experience, it happens in traditional classrooms as well. In a few cases, a multiple-choice question had more than one correct answer. In other cases, selecting the correct answer would not give you credit. The Coursera staff would eventually fix these problems.

In contrast to the Functional Programming class, where your grade was completely determined by unit tests and static analysis against your Scala code, the Social Network Analysis class was more “traditional” in that there were multiple-choice-style homeworks and a timed final exam.

Because the class is free, the measure of its value is, “Is taking this course worth my time?” For me, the answer turned out to be a definite yes. I probably spent about 5 hours a week listening to the lectures and completing the assignments. Prior to taking the class, I spent some time reading about the subject matter. I found taking the class to be a more efficient way to really understand what the field was about. For that reason, I recommend it to anyone who needs to interact with networks, social or otherwise, and is not familiar with the field.


  • Guest
    kavitha Tuesday, 27 August 2013

    I want details about social network analysis

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