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Applied Social Network Analysis in Python に戻る

ミシガン大学(University of Michigan) による Applied Social Network Analysis in Python の受講者のレビューおよびフィードバック



This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python....



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.


Applied Social Network Analysis in Python: 326 - 350 / 411 レビュー

by Thomas L


Course was very straightforward application of the lecture materials. Not as challenging as the first three courses of this specialization, but nevertheless it was instructed very clearly and was informative. Would recommend this course.

by Srinivas R


Good overview of network concepts using networkx - wish the course were a few weeks longer for it finishes just when you feel you can begin to something useful with the basics you have learned - but you do learn the basics.

by bob n


Good basic course, well paced. I liked the instructor. Weekly assignments fair, some tougher than others. Occasionally finicky Auto grader a bit like artillery, need to send a couple of rounds over to home in on target.

by Devansh K


Extremely detailed and challenging course. The assignments require a lot of thinking and skill. Gives a comprehensive overview of social network analysis and a good way for any novice python coder to improve their skills

by Bernardo A


Really good overview of concepts and analysis related to 'graphs'. Could be more challenging when it comes to projects: for example, teach students to gather real data from twitter or facebook and make graphs with it.

by Chris M


I know its hard to go in deep detail with these courses. If you used one graph and gradually built upon it through the course it may reinforce the concepts better. Thoroughly enjoyed though, learned a lot.

by Chad A


The material and assignments were great and well aligned. The autograder for the Jupyter Notebooks was finicky at best and resulted in lots of time wasted getting formatting correct.

by Vivien A


Great content but assignment / auto grader sometimes difficult to deal with. In particular, errors not clearly described. Much time wasted due to wrong package version, etc. etc.

by Eric M


This was an excellent overview of using and analyzing graphs with Python. I learned a lot, got to apply my learning from previous courses, and I earned my Specialization!

by Raul M


Great class for an introduction to networks.I didn't give it 5 stars because it didn't give me enough information to apply the concepts learned to real life projects.

by Vishal S


Lectures are very well-designed. Especially, the assignment of week 4 is too good, that give me an overview of how we can apply machine learning in network analysis.

by Steffen H


Course was ok, the assignments are not too difficult. I wish the course would provided more insights and discussions of the presented metrics of centrality though.

by Sean D


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.

by Ezequiel P


Great course! The topic is very interesting! I would have liked it to have more hands-on approach during the lectures, but the course quality is great



The presentation documents are very helpful to understand the lectures. If they can be downloaded to our local laptop, I evaluate this course 5 stars.

by Alejandro B


Great course, however, there is quite complicated the autograder system. Sometimes it takes too much time trying to figure out technical issues.

by Martin U


This was a great course, lots of great insights to gain. Only thing that was frustrating was the multiple choice quiz questions. I hated those.

by Tom M


A bit confusing material since it is new to me. Lots of material in a short course. The auto grader is a bit difficult to work with.

by cadmium


The course provides a good overview of basic measures for network data. I took as prep for a harder course. I would recommend it.

by Dmitry B


This course was easier that the previous 4 in the specialization as it used them as a foundation for practical graph analysis.

by Victor G


Intreesting and rich in learning. The last assignment was specially fun. Would be nice with more such free assignments.

by Daniel D A


I liked the lectures but the assignments were significantly harder and had content that we didn't learn in the lecture

by Lucas G


Nice overview of general graph theory, and some useful exercises on how it can be applied for social network analysis.

by Yu C


This instructor in a lot better than the one in the text mining course, and the course content is better prepared.

by Mike W


If you've had prior expose to graphs (e.g., an intermediate-level CS course), the first 2.5 weeks is pretty easy.