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.
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.
by Rahul S•
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.
by Rishabh M•
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.
by Subramanian A•
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 !!
by Abu S•
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.
by Yusuf E•
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.
by Jonathan B•
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.
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.
by Keary P•
Nice way to end the 5 course specialization. Brought together several machine learning and python skills that I learned in the previous courses. Instructor does a great job introducing new concepts with high level theory and intuitive examples. Course slides were superb and can serve as future reference material.
by Ricardo S•
Great course. Clear content, both on theory & practical applications giving a good overview of Graphs/Networks analysis as well as Simulation. I enjoyed the programming exercises and in particular appreciated the possibility of using ML algorithms for prediction within a Network framework.
by Víctor L•
Excellent Course, very interesting, no idea that so many tools existed for network study and analysis. Excellent job both from the professor Daniel, and from Coursera/University of Michigan State. The QUIZES were very challenging, sometimes more than the Assignments. I'm really satisfied.
by Niranjan H•
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.
by Santiago D D•
This class was an excellent introduction to network analysis, where concepts, metrics and purpose of application where provided in a clear and digestible manners. The instructor made the class very livable with topics that might have been too dry under different circumstances.
by Carl W•
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.
Eventhough the tutorial video is also switch to the teacher's face that make me stop the video to see the slide frame.But It's intuitive to understand the basic concept about the network with some exercise to enforce the knowledge. The final exercise is more intersting...
by Praveen R•
I learnt about networkx and its capabilities. The course introduces to many network algorithms and talks about concepts of centrality, page rank, etc. Good eye opener to all these concepts. The last assignment is very practical and challenging. Enjoyed the course.
by Dongliang Z•
I enjoyed this course. This course is about the basic knowledge in network analysis. I do hope the lecturer can give more knowledge and application in network analysis. (Perhaps holding a series courses of Network Analysis in Python will be very good in the future!)
by Dung D L•
Wonderful course with plenty of amazing knowledge about Graph and Network that I have never been approached. After this course, I have several skills to apply to my job. I truly appreciate the teachers, TA, and all people who contributed to this course.
by john w•
Well put together. Quizzes test on material covered and assignments expand on it. There is still challenge and rigor, but it comes from understanding the concepts, not ambiguity and lack of instruction. This is one of the best online courses I've taken.
by Nikolay S•
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.
by sampath A B•
I have really enjoyed the course ("Applied Social Network Analysis in Python."I like the way you summarize each module at the end of the module. I think others should learn from you.However, the python "Networkx" library is very annoying.
by Juan C E•
Excellent course. Very clear explanations and materials. The assignments were not as difficult as in other courses of the specialization, and very helpful to understand the contents. I highly recommend this course and the specialization.
by Ari W R•
It is a little bit harder to finishing this course, but i really enjoy it. There're many useful things that we can get from it. I hope always remind this experience about this knowledge and can implemented in the future. Thank you!
by Manuel A•
Very challenging and comprehensive course, also directly applicable to machine learning problems, as an example, the last assignment applies network knowledge to extract features and exploit them in predictive modelling problems
by Alexander G•
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.
by Ling G•
I like this class because the topic is interesting and the homework is not too hard but walks me through some important functionalities of NetworkX. The instructor is also pretty good at presentation as well.