Learner Reviews & Feedback for Applied Social Network Analysis in Python by University of Michigan

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.

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.

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.

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.

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

it is good but we are looking for more real practices

An excellent course that provides a fair knowledge of social networks, the NetworkX package and how to work with networks in Python.

great

very useful and engaging. A was hoping a very different scope on the course but this approach did very well.

Just the greatest class to sum up what is going on in the crazy network media and using what we have learned.

The course and the tutor are great.

I learned how to create and manage network graphs using python with networkx. I was really satisfied from the last week assignment when I had to work with real-life example plus machine learning classifier.

Very useful

A completely new area for me, and a really fascinating course.

I got a bit the wrong impression from the title, but it was throughout the course very interesting to learn about Graphs. A welcome addition to the course would be a cheat sheet with the most important quantities.

Nice course. Well presented.

Good course, may be better if offer more practice and application.

Great course

As a course by itself or as part of the specialization, either way (it helps to have completed the first two in the set), it is a great course.

It provides a very good high level picture of what is needed in ones toolbox.

Essentials: networkx, matplotlib and to a lesser extent pandas.

Great course!

This is a great course for 2 reasons. The earlier assignments were just difficulty enough to reinforce the lectures. The last assignment was challenging enough to bring the entire specialization to to satisfying close. After finishing assignment 4, I really feel that I can apply the learning from this specialization to real work.

A nice intro to networks in Python

Excellent delivery and content.

The course covered many relevant topics and was very easy to follow and apply to the real world.

good