types of social network analysis courses
We live in a golden age of network analysis. It’s booming as science and booming as business. This raises questions for the teacher – what course should you teach? A few options:
- Bare bones: A course designed for folks with little to no mathematical background. You would teach descriptive stats, visualization, and applications.
- Stats+/Models : In this course, you’d assume some basic background. Maybe micro for econ students or stats for other social science students. Then, you’d dig deep into different centrality measures, power laws, clustering/community detection, etc. A follow up course would deal with p*, ERGM, Sieana and other advanced issues.
- Programmers: Here, you’d lightly gloss over the math and proofs and instead focus on how to scrape the net for data, how to write simulations, and how to manipulate big data sets.
- Elite stats: This is for a very small number of students in math, stats, or econometrics. It would be exclusively proofs of fairly advanced issues (like the graph models underlying p*).
Currently, I teach a course for sociology seniors between 1 and 2. I get soc students, a handful from econ/psych/poli sci, and one or two informatics students. I also get one or two grad students. The elite soc programs, where students often have science backgrounds or simply a lot of mojo, are now seeing Programmers courses. Old school networks courses (a la Ed Laumann or John Padgett at Chicago) offer a version of #2. Elite stats is exceptionally rare in that if students are that advanced, they can often read the papers themselves. Add your own comments about networks education.
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