Ratings and Reviews for Applied Social Network Analysis in Python

Great course, very informative. Thanks!

The best course I had on coursera ever, it really broaden my scope of knowledge. It worth waiting so much time

The first three weeks are very well planned.

This was, in general, a good course. The instructor was very clear in what he presented, and gave a good overview of Social Network Analysis. However, there were several issues with the AutoGrader that did not get fixed until late in the course and the PowerPoint slides for the lectures were also very late in getting posted (they were not available for most of the programming assignments). So, I think this course was launched a little early. Still, these are problems that you might expect to see the first time a course is taught and should not affect future students. The bigger complaint I have on the course was that it was a very gentle introduction of the topic with only a quick overview of the subject. The lectures themselves concentrated more on a litany of various measures and metrics to characterize networks and could have benefited from a broader examination of real networks in the real world. One of the most interesting topics was a very quick overview of plotting for network diagrams, but this was never followed up with a programming assignment or other aspects to give us practice using the techniques described. This course would benefit from 2-4 additional weeks of material and more programming assignments, IMO. The network graphing lecture, for example, could have been reinforced with a peer-graded assignment to have us produce 3 or 4 types of graphs of various networks. Overall, though, I was pleased with this course and the entire specialization. I would definitely recommend it to others.

老师讲解的非常好 ， 逻辑清楚，条理明晰。建议编程作业稍微有点难度。所以扣掉一颗星。 希望越来越好。

Nice overview of general graph theory, and some useful exercises on how it can be applied for social network analysis.

Excellent course. Lecturer clearly explains network analysis terms and algorithms with examples, and then shows how they are implemented by the Python networkX library. The assignments exercise their use.

I like this class because the topic is interesting and the homework is not too hard but walks me through some important functionalities of NetworkX. The instructor is also pretty good at presentation as well.

Learnt considerable amount about social network from this course, as introductory level, materials (lectures and assignments) are well-prepared, much better than course 4 (text-mining). Assignments are not too hard, probably has relative good foundation from previous 4 courses. Auto-grader is a real pain in this specialization (course 3, 4 and 5), need to go through thorough test before release. Do not consider this specialization as intermediate level.

Pro: Required interpretation of methods presented for application on assignments without explicit direction. Required application of knowledge gained in previous specialization courses. Con: Explanations of social network analyses were limited in number and shallow in coverage.