All assignments and lectures are outdated and will not work with current versions of Python.

They need to change the 4th assignment is almost impossible to run on jupyter

Vague, little explanation, I can get a better education on Udemy

Week 2's quizzes are highly irrelevant to the content taught.

Obsolete course

Not updated.

Well, the actual score is more like 4.5 stars since I still rate the Machine Learning course by Andrew Ng as the best course I've taken on Coursera. Anyway, this course in my opinion is the best course in the specialisation and I'm glad I stuck around for it.

Pros: the instructor has a very good delivery, and explains concepts in sufficient depth gradually, I really like the way he explained how some measures are calculate using step-by-step examples showing which nodes/edges are being used. Compared to Professor Brooks who either gives very superficial lectures on what matplotlib can do (line graphs) to suddenly going into the technical details of the different matplotlib layers

The assignments are the most reasonable I've seen in this specialisation, tying relatively well with the course lectures (though Week 3 assignment's might be tad too simplistic).

Con: Outdated autograder as usual, however this was probably the mildest case compared to Course 1 (Intro to Data Science) where so many things were different between the old and new pandas version.

Here's my personal overall ranking of each course in this specialisation from best to worse:

1. Social Network Analysis

2. Applied Machine Learning

3. Applied Text Mining

4. Intro to Data Science

5. Plotting

So, I passed all modules in the whole specialization and received the certificate. This is by far the best course, and the reason for this is the instructor. Daniel Romero is great at explaining the concepts, expresses himself clearly and uses lots of examples which help immensely. The programming assignments are actually fun to solve - the instructions are clear and well-formulated. I know what is expected and can focus on doing data science. For the first time I didn't have to spend hours reading the Discussion Group posts in despair, in order to figure out how to pass the assignments (tricks, hacks, etc). This can't be said about assignments in other modules. I think the assignments were not too easy - to me the difficulty was just right. It's an introductory course to this matter and the worst you can do is daunt learners with unrealistic assignments (as in Week 4 of Text Mining). I think my appreciation for this course is intensified by the irritation with other courses. But at any rate, great job Daniel.

Daniel Romero is easily the best instructor in this specialization, beating all his more senior colleagues in communicating knowledge to learners. I especially liked his methodical approach to telling the whole story: big picture intuition, formal definitions, Python examples and practical applications.

The course sets you up with a solid enough baseline in networks to be able to continue learning on your own. It is a big plus that the final assignment is really pulling together your knowledge from most courses in this specialization (there is no visualization required in the final assignment).

My only complaint is that the course seems like somewhat of an afterthought to bring the course count to 5 in the specialization. The content in week 4 is particularly short. I would rate this course 4 stars on its own, but given how it lines up in the specialization it is an easy 5-star.

nice course with good content in quizzes and assignments. The last assignment was great and very practical (well framed question, which uses ML algorithms to predict node attributes and linkage using various network measures as features). Overall a pretty much useful course using graph theory and a practical course. Most of the assignments are concentrated on how real world problems could be?

The centrality measures are explained beautifully. The module four is pretty much the heart of this course.

I have really enjoyed learning this course. All the concepts are explained with proper examples. This course not only provides theoretical knowledge about network analysis but also explains the use of each topic in real networks. The assignments were really helpful to get hands on experience of all the topics covered. The most interesting part of this course was the last assignment It was fun experimenting with different models and analyzing the performance.

really interesting introductory course.. originally took it just for credit transfer, but ended up being interested in the topic.

I'm from engineering background, though not computing. Some coding did take me some time to finish. Nice degree of challenge to stretch me. Overall glad that I took this course. Got to know that social network is not only used in social network, but can also be used to analyze flight & airport network, optimize search engine etc.

This is the last course of the Applied Data Sci in Python certificate. It effectively ties together all the introduced concepts from the previous courses (except Natural Language Processing). Daniel Romero was an extremely effective lecturer and many of the concepts and know-how were introduced, taught, and assessed appropriately. I'm also impressed that I was able to learn a new python library I (or my coworkers) had not heard of before.

I learned many interesting new concepts in social network analysis and a bunch of new graph algorithms, which are rarely taught in the "traditional" algorithm course. Now I know how companies like Cambridge Analytics can use the Facebook's social network data to derive useful information. (It's actually quite easy.) A class like this is more important than ever. I just wish we could have more time to explore a few topics more deeply.

This course is a great way to learn about networks, how to build network models, and techniques to analyze them. The focus on applying fundamental concepts was useful, especially how network models can feed machine learning models. However, the course didn't cover accessing and analyzing data from popular social networks at all. And the course uses version 1.11 of NetworkX which is woefully outdated. A course update is badly needed.

Everything in this course was new to me. I was always curious about social media products and how companies like Twitter and Facebook come with certain features in their offerings. This course is very introductory but it provides a good platform to develop interest and pursue more knowledge in social network analysis. I highly recommend this course to learn to decode social network analysis.

I come from all the courses in this specialization and for me (as a begginner in Graphs theory) the course was absolutely relevant. The instructor really caches me and the assigments through the course were good enough.

The week 4 was specially important to ground all the knoledge in the course and specialization (mixes Machine learning, Graphs and Pandas).

Great course !

This course was very interesting and well taught, finally after all other courses I have managed to complete the assignments for this one in the recommended amount of time. Maybe the questions were structured better than past modules, or maybe my level of understanding of programming in python was at its best. Either way the assignments were very enjoyable, thank you!

Very good course, networks can be used in almost every aspect of a business or market. We learned many ways to represent networks in python, and visualize them. The lecturer was very direct and to the point with his slides and examples; the summaries after each lesson are so useful. I would like the final assignment to be a lilttle more challenging, though.

Remarkably good explanations, and interesting selection of subtopics. Interestingly , it does not delve into Facebook or any other social media applications, and is still just as valuable as it covers Graphs in some depth. Uses Python and its NetworkX library. Knowledge of classification models and scikit-learn is needed for the 4th assignment.

Excellent Course and Specialization. I learned a lot of techniques and tools through this specialization. The specialization has provided a new dimension to my knowledge and learning. Assignments were amazing. The cherry on top of the cake was last assignment of the last course, in which we used the knowledge from the first course to the last course.

Excellent course with a broad overview of the networks an how python packages can be used for network analysis. There was a nice mix of conceptual sessions along with the usage of networkX for coding assignments. Thanks to UMich for putting this course together !! I put some of the concepts to work right from the day I learnt them. Awesome !!

I started this course with certain amount of nervousness since I did not have a lot of idea about network analysis. With time I really become interested in this subject and by the week 4 I was really fell in love with this subject. The teacher was very engaging and clearly explained the ideas. Looking forward to finishing the specialization.

Coming into this course, I didn't expect much but I was pleasantly surprised by the quality of the material. The quizzes were especially designed well and the final assignment was really challenging and instructive. I wish there was more of predictive modeling using network features but the rest of the course easily makes up for that.

I only took this course so that I could finish off the data science specialization and I was pleasantly surprised by how much I enjoyed it. Instructor did a great job of tying the content to real-world applications and I personally enjoyed the final project which utilized much of the material that was learned throughout the course.