Extremely good introduction to network analysis. The course heavily relies on NetworkX, and doesn't require extensive programming knowledge - with the help of Google, you may easily solve all problems. The lectures were well structured and easy to follow. Having said this, I have found 2 major drawbacks: 1. I would really appreciate some external references so that I could get a theoretical introduction to the materials taught. 2. The last assignment required machine learning, which was not taught in this course. With the help of the forums and a bit of googling, it is easy to get full mark, but perhaps the authors could include such background in the provided notebooks?

The explanations were very really good and clear but not enough to complete the assignments. The assignments were over the top in difficulty. The hardest in the entire course program. That is the only reason I took one star. It was because I felt that the classes did not prepare for the assignments. Or, assignments should have a more clear explanation of the steps to be taken in order to complete them. Definitely we should look for answers ourselves but not being able to clearly understand each step throughout the assignments really limited my research area and increased my frustration.

I started the course only because it was part of the Specialisation, but I am glad I did because the topic is actually very interesting! This course covers the basics. The lectures are very well structured, quizzes are suitably challenging, and the assignments are interesting while not terribly challenging. You'll apply some of the machine learning concepts from course 3 in the final week's assignments, which I though was a nice, round finish to the Specialisation.

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.

The professor was great! The way he explained everything was clear and understandable. The mentor, Uwe, in the course was also super helpful when other students or I needed assistance during assignments. The only reason why I gave this course 4 stars rather than a 5 was that there were many errors in the assignments and auto grader.

Good introductory course for conceptual understanding of Network Analysis in Python. Good assignments, would be great to have more real world examples and hands-on business case studies for application of Network Analysis in different Industries. Also, they should update the course to use recent version of Networkx - 2.8 library.

Its a amazing course for beginners with little Python experience. The lectures and quiz are simple and assignments are really challenging. If you are looking for Social Networks course which covers nook and corners of Social Networks Analysis then this course is not for you.

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.

The course starts off a bit slow but gets you used to the NetworkX module. The last exercise is a pretty neat culmination of the this course and specialization. It would have been cool for it to also involve text mining, but I enjoyed it and the course in general.

The course provides a great introduction to graph analytics, I consider that the social network applications are very sparse or missing in action altogether. Nonetheless, overall great content and practice of extracting information from networks with Python.

Social Network was completely new to me and I found this course provided basic and more detailed information about the matter, and also enough documentation to continue learning. I see there is much more to learn, but the course was a great introduction.

Course was very straightforward application of the lecture materials. Not as challenging as the first three courses of this specialization, but nevertheless it was instructed very clearly and was informative. Would recommend this course.

Good overview of network concepts using networkx - wish the course were a few weeks longer for it finishes just when you feel you can begin to something useful with the basics you have learned - but you do learn the basics.

Good basic course, well paced. I liked the instructor. Weekly assignments fair, some tougher than others. Occasionally finicky Auto grader a bit like artillery, need to send a couple of rounds over to home in on target.

Extremely detailed and challenging course. The assignments require a lot of thinking and skill. Gives a comprehensive overview of social network analysis and a good way for any novice python coder to improve their skills

Really good overview of concepts and analysis related to 'graphs'. Could be more challenging when it comes to projects: for example, teach students to gather real data from twitter or facebook and make graphs with it.

I know its hard to go in deep detail with these courses. If you used one graph and gradually built upon it through the course it may reinforce the concepts better. Thoroughly enjoyed though, learned a lot.

This course covers a great deal of topics and gives a great deal of experience in learning how to use and understand a variety of visualisations, machine learning algorithms and social network analysis.

The material and assignments were great and well aligned. The autograder for the Jupyter Notebooks was finicky at best and resulted in lots of time wasted getting formatting correct.

I really like the content of the course.

What needs to improve is the networkx package is used in the lab. It's an old version with old functions and they are not working sometime.

Great content but assignment / auto grader sometimes difficult to deal with. In particular, errors not clearly described. Much time wasted due to wrong package version, etc. etc.

This was an excellent overview of using and analyzing graphs with Python. I learned a lot, got to apply my learning from previous courses, and I earned my Specialization!

Great class for an introduction to networks.I didn't give it 5 stars because it didn't give me enough information to apply the concepts learned to real life projects.

Lectures are very well-designed. Especially, the assignment of week 4 is too good, that give me an overview of how we can apply machine learning in network analysis.

Course was ok, the assignments are not too difficult. I wish the course would provided more insights and discussions of the presented metrics of centrality though.