If you are following Dr. Severance's Python course series, expecting a similar experience, be ready for disappointment. Where Dr. Chuck's series is well-taught in-depth information with great examples and explanations, this course series is basically a summary and the instruction to go out and learn everything yourself from the web. That's an abysmal way to teach, because for beginners to data science programming, there is no basis to determine which is bad or good information you pick up from a blog or Youtube.

This class is a perfect example of just that, but worse, because there's an implicit trust in a professor from a university the caliber of Michigan.

The lessons are old, and they won't update them. The forums are full of bug reports and information about code that works when tested outside the class environment, because the class is taught on networkx version 1.11, which was released in 2016. I'm writing this in April of 2020, and networkx 2 was released almost 4 years ago, and this class is teaching techniques that no longer work because the code is deprecated.

In every course in this series, I wasted long hours trying to debug what I was doing wrong in my assignments, only to find out that my code was perfect - today, and the grader was testing my work with functions that are no longer valid.

I know reviews can be biased, especially negative reviews, so please read the forums before enrolling in this course and wasting your money. The quality of education from this series is very poor, and you are better suited to learning from Youtube (sadly) if you want actual information you can use in your work.

Unless you are looking for an education so that you can go back in time and find a job in 2015, this course is a waste of time and money.

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.

The specialization for Applied Data science started strong, with engaging exercises, good instruction, and good recommendations for additional reading and resources. As the specialization continued, the courses seemed to get "lazy", and the course topics became more abstract and less applied.

After going through this specialization, I would not recommend this to someone if I could find a better program through edX or another coursera offering.

For a coding heavy course, why doesn't the instructor just upload the code used in slides as a Jupyter Notebook? This would save A LOT OF TIME and frustration. Right now, I have to pause the video to copy the code AND write my own notes and it wastes so much time. Not to mention, you can easily be prone to writing wrong syntax when you're trying to keep up so fast, and then you run the code chunk and it doesn't work and you have to go back to that point in the video. It's a simple staple that I would have expected in a UMich course. Also, they don't show how to create networks from pre-existing data, which is how you will usually work in the real-world

The assignment auto grader was horribly designed.

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.

It was an easy introductory course that is well structured and well explained. Took me roughly a weekend and I thoroughly enjoyed it. Hope the professor follows up with more advanced material.

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

Well made intro to networkx library and some basic graph concepts.

It seems that course material has been done 3 years ago, and has not been touched since. The age is showing bit. For example autograder has small issues which could be fixed to remove lot of frustration and extra work from the studet

For example sometimes when returning pandas Series, the autograder requires that it doesn't have a name, but the autograders feedback doesn't give any indication about this. Minus one star because of these. All the courses in Applied Social Network Analysis in Python by University of Michican has this same issue.

The Course Deserves 5 Stars BUTThe fundamental flaw that felt absent in the last two courses of the specialisation was the in lecture Jupyter Notebook Demonstrations, it really helped the students feel in sync with the mentors.Please correct the same all the 5 courses of this specialisation deserve 5 starts :)

I think that assignments 1-3 were too basic; often, you only had to return a simple function which outputs a specific network metric. Assignment 4 was a lot better, as it comprised the necessity to apply knowledge from previous courses. The instructor did a good job explaining the different concepts!

This course is a excellent introduction to social network analysis. Learnt a lot about how social network works. Anyone learning Machine Learning and AI should definitely take this course. It's good.

Excellent tour through the basic terminology and key metrics of Graphs, with a lot of help from the networkX library that simplifies many, otherwise tough, tasks, calculations and processes.

Really enjoyed the mathematical component of this course. It was fun to see how you could connect the graph theoretical components to the machine learning concepts from earlier courses.

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

Maybe I was getting used to the grader, and getting better at Python, but I found this the least frustrating of the specialisation, with a lot less time wasted on grader issues and the like.

Its a good general introduction to the theory, with some good exercises which combine network analysis and machine learning.

Like all of these classes, it is getting a little long in the tooth (2016, so a good four years old now, in a very fastmoving field), and it would be nice if it was updated a little, but given that its basic stuff, it is still very relevant.

It would also have been nice to have had a challenging, bring it all together, project at the end of this, but I guess its my job to find one now!

Did not make clear the prerequisite requirement to understand machine learning classifiers using sklearn library. Had to go back and teach this to myself in a week in order to complete finial assignment. This should be in the prerequisites for the class.

I liked the lecturer and the tempo of the lectures, but this course felt a little light compared to the others in the specialization. The quizes were also good. But for me the course was a bit off topic. Given that, the various skills I learned in the other courses did come together in the final programming assignment. As a stand alone course I would give it four stars, but it gets three because it's required for the data science specialization.

better if TA could prepare projects with updated versions of libraries

it is good but we are looking for more real practices

All the assignment have issues,.

Negatives: A LOT of poorly explained theory and not many exercises. Many mistakes in the slides and codes, and you needed to find the solutions in the discussions. The Autograder was a bit annoying because sometimes you'd spend 20% of the time getting the right solution to the questions and 80% fitting it so the Autograder would understand it. I had to look for Youtube explanations to do the assignments and quizzes because based on what was explained I either didn't understand it or simply couldn't find it in the source material.

Positives: You get a certificate from the University of Michigan.

Python and related libs are SUPER old. Some important codes used in this specialization were duplicated and you cannot get enough explanations even on Google. Moreover, auto-graders have lots of problems. I finished this specialization but I cannot recommend this.