This course is a good introduction to graph theory. Its contents are interesting and the lecturer did a great job explaining them. So, what is the problem? The problem is that the course is not called "Applied Graph Analysis in Python" but "Applied Social Network Analysis in Python". This incongruity in the title of the course (intentional or not) will generate erroneous expectations in the students, especially if we consider that they have to take the course to finish the specialization. Regarding the assignments, they are divided into two groups: trivial tasks that are solved with a single line of code extracted from the NetworkX manual and more complex tasks related to Machine Learning that do not involve putting into practice the concepts of this course but those of the third course of the specialization. I regret being so tough, but my impression on this course is that it is filler content designed just to have a five course specialization instead of four.

Going into this course, I was really disappointed that I had to take this course for a Data Science Specialization because at a skin-deep level it seemed very irrelevant, and frankly I was at that state of mind until week 4 of this course.

There are several reasons why I'm rating this course 2 out of 5 stars:

1) The content of the first three weeks were just informational and should have been covered in one or two weeks.

2) Homework assignments were not challenging at all. 90% of the questions were one-liners and required simply calling the methods of networkx that was discussed. This course would benefit by homework assignments that had 1-2 problems that required us to solve real-life problems from scratch, rather than ONLY calling networkx methods.

3) There was no discussion on how to get network data. We were just given all this magical data about how relationship scores between employees and future connections between employees... How am I supposed to get that in real life?? Some problems asking us to make a network would've been valuable.

4) More time should have been spent on prediction and other advanced topics, at least another week to bring the "Applied" into "Applied Social Network Analysis."

5) I really enjoyed the professor's teaching style. He explained concepts well and had great examples during lectures.

Great course, maybe even the best on this great specialization!

This is by far my favorite Coursera course - well organized contents and intuitive example!

This is hands down the best taught course in the speciality. The instructor explains concepts in the videos clearly and the assignment questions are structured and interesting. Do note that the assignment in week 4 does pull together the whole specialisation in a real world problem, so if you aren't taking the whole speciality you will need a knowledge of Pandas and SKLearn. Personally I thought it was pitched at just the right level because the ML work is just enough to have to go through the process, without any complicated feature optimisation.

Only wish the other courses worked as well as this one.

The lectures are good. However, the assignments are poor: very simple exercises with toy examples, but far away from real applications. Moreover, I spent most of the time (particularly in the last assignment) trying to deal with the autograder.

Interesting.!

this course provided a overview of the network analysis.

Overall, good course. It could use more explicit examples of NetworkX in the actual Jupyter Notebook itself, but the coverage of the material is high quality.

Daniel Romero is probably the best instructor in this specialization

Great course

My favourite course

Amazing course!!!

Great Content!!

One of the best courses on social network analysis. Professor Daniel Romero did an excellent job explaining the contents.

This was a good course. I learned a good amount about network analysis and the python library networkx. I can envision using what I learned in my job. However, of the five courses in the Applied Data Science with Python Specialization I felt this was the weakest offering.

1. The Title. While the majority of the examples and exercises were focused on social networks, there's little in the course that is really specific to social networks. The course applies to any kind of network that can be loaded into networkx.

2. Trim the Process Descriptions. Too often the lecturer would say things like "Node A has degree of 3 because it is connected to three other nodes. Node B has a degree of 5 because it is connected to five other nodes. Node C has a degree of 4 because it is connected to four other nodes." For such a simple concept, that many examples aren't needed.

3. Provide On-Screen Example Files (my biggest gripe). In all of the previous courses, when the lecturer gave code examples on screen, there was a corresponding Jupyter notebook with those examples so the learner could follow along, and keep the notebook as a handy refresher of how to interact with the library. None of that was provided in this course.

The machine learning connection could have been mentioned earlier in the course

Very helpful, I didn't know anything about graphs, networks modelling and the NetworkX package before this course.

i found this course to be fun and straightforward. The assignments were directly aligned to instruction. Great course!

Very very good course. It provides a brief but comprehensive introduction to network analysis.

I found it hard sometimes to understand the concepts but this gave me quite an introduction on social network analysis and encouraged me to learn more about them.

great!!

Month 5 was very nice. I enjoy networks and appreciate your presentation of the material. I would also like to thank all of those who worked to bring the specialization to life. This includes the lecturers, grad students, and mentors who devoted time to the class.

THANKS!!

Great course with clear instructions

Great intro to networks; last assignment is challenging but is a good opportunity to put everything together (python+ML+Network).