There are many curriculum problems with this course:

1. Although it was advertised as a Beginner course, much, if not most, of the course was laden with mathematics up and including calculus....often dismissed by Prof. Lee as "straightforward, simple math".

2. The slides were well short of being self-explanatory.

3. Many of the slides had editing mistakes and diagrams or graphs mis-labelled.

4. Several of the lecture videos did not have corresponding slides as part of the student package.

5. In my view, Random and Scale-Free Networks were not well-handled.

6.. Some of the exam questions were either not covered or the topics well-enough taught in the course to handle all of the exam questions competently.

7. Optimality (which was included as one of the exam question options) was only passingly mentioned but not well-defined.

8. Characteristics, distinctions and pros and cons of using the range of network models was not well explained. The Jin-Girvan-Newman Model, which was one of the exam question options, was not even mentioned in the course.

9. I can't access the apparent feedback on the Final Exam to find out what two questions I didn't answer correctly.

10. Overall, the editing of this course was very poorly done.

In summary, it appears that this course was thrown together, or culled, from another, more advanced course, designed for another completely different...and it isn't clear who the audience was for this course. The underlying audience appeared to have been for a group of physics or complexity science students with a strong math background. Although I have 2 post-graduate degrees (M.Sc. in Molecular Biology and an MBA), this course gave me flashbacks of the boring, irrelevant applications and approach I remember very well in the way my under-grad math courses were taught (often by professors with a physics or engineering bent)! I have taken 3 other Complex Systems MOOC courses and, unfortunately this is the one I enjoyed least, due to the aforementioned reasons. I am not a whiner by nature and have a teaching background (including curriculum design and development), including at the university and post-graduate level...I answered 14 if the 16 questions correctly.

It is for this reason that I didn't rate this course highly (3 out of 5, and I'm being generous), although I appreciated the efforts made by the instructors and although it was still worth my effort. This feedback is not intended to de-energize the course instructors but is offered as constructive feedback and to challenge the instructors (and department) to "raise the instructional bar" in further appetizing folks to Complexity Science. 

The course videos are very good, although some of them are taken from live classes and not made for the course, the jupyter notebooks activities are a big plus (you need to know python to work with them). Unfortunately, there is little connection between theory and practice, there is no graded task to perform and the final exam quiz has no feedback, you don´t get to know what you answered wrong (maybe a bug, it seems to score always 75% matter what you answer).

The course provides an easy approach to a laymen to being exposed to the study of complexity science. This broadens and opens up insights to learning.

A lot of really interesting detail that you don't get in many other courses around complexity. I particularly liked learning about Soup-of-Groups.

Great introduction to the topic!

recommendation

I preferred more projects to be added in the program.

No assignments or exercises to practice every week. Confusing materials. Not a well-structured course.