Ratings and Reviews for Applied Social Network Analysis in Python

Very good experience

Very informative and useful content was presented in very easy to understand way.

very well explained thank you very much

Great teaching!

I really appreciate Coursera for offering this course. It is very valuable to my research.

Great course. Clear content, both on theory & practical applications giving a good overview of Graphs/Networks analysis as well as Simulation. I enjoyed the programming exercises and in particular appreciated the possibility of using ML algorithms for prediction within a Network framework.

nicely explained

Well, the actual score is more like 4.5 stars since I still rate the Machine Learning course by Andrew Ng as the best course I've taken on Coursera. Anyway, this course in my opinion is the best course in the specialisation and I'm glad I stuck around for it. Pros: the instructor has a very good delivery, and explains concepts in sufficient depth gradually, I really like the way he explained how some measures are calculate using step-by-step examples showing which nodes/edges are being used. Compared to Professor Brooks who either gives very superficial lectures on what matplotlib can do (line graphs) to suddenly going into the technical details of the different matplotlib layers The assignments are the most reasonable I've seen in this specialisation, tying relatively well with the course lectures (though Week 3 assignment's might be tad too simplistic). Con: Outdated autograder as usual, however this was probably the mildest case compared to Course 1 (Intro to Data Science) where so many things were different between the old and new pandas version. Here's my personal overall ranking of each course in this specialisation from best to worse: 1. Social Network Analysis 2. Applied Machine Learning 3. Applied Text Mining 4. Intro to Data Science 5. Plotting

The lecturer was the best of the lecturers in the specialisation.

nice course with good content in quizzes and assignments. The last assignment was great and very practical (well framed question, which uses ML algorithms to predict node attributes and linkage using various network measures as features). Overall a pretty much useful course using graph theory and a practical course. Most of the assignments are concentrated on how real world problems could be? The centrality measures are explained beautifully. The module four is pretty much the heart of this course.