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

4.6
stars
2,681 ratings

About the Course

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....

Top reviews

NK

May 2, 2019

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.

JL

Sep 23, 2018

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.

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326 - 350 of 452 Reviews for Applied Social Network Analysis in Python

By SHREYASHI D

•

Sep 17, 2020

great

By Yash B

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May 24, 2020

great

By Muhammad M M

•

Jan 26, 2020

Good!

By Deleted A

•

Dec 5, 2018

great

By Gerardo M C

•

Nov 17, 2017

Nice!

By Maxerom24

•

Dec 25, 2021

Best

By WANG Y

•

Aug 28, 2021

good

By Ankit K G

•

Oct 25, 2020

good

By Gudimetla v n r

•

Sep 20, 2020

nice

By Murugeswari P

•

Aug 13, 2020

good

By RAGHUVEER S D

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Jul 25, 2020

good

By Heshan L

•

Jul 17, 2020

good

By SUTHAHAR P

•

Jun 2, 2020

Good

By Hewawitharanage A H

•

Jan 31, 2020

good

By Parul S

•

Apr 20, 2019

good

By Akash G

•

Mar 3, 2019

good

By Deleted A

•

Aug 17, 2018

Wow

By Magdiel A

•

May 11, 2019

ok

By David C

•

Sep 21, 2017

This was, in general, a good course. The instructor was very clear in what he presented, and gave a good overview of Social Network Analysis. However, there were several issues with the AutoGrader that did not get fixed until late in the course and the PowerPoint slides for the lectures were also very late in getting posted (they were not available for most of the programming assignments). So, I think this course was launched a little early. Still, these are problems that you might expect to see the first time a course is taught and should not affect future students.

The bigger complaint I have on the course was that it was a very gentle introduction of the topic with only a quick overview of the subject. The lectures themselves concentrated more on a litany of various measures and metrics to characterize networks and could have benefited from a broader examination of real networks in the real world. One of the most interesting topics was a very quick overview of plotting for network diagrams, but this was never followed up with a programming assignment or other aspects to give us practice using the techniques described. This course would benefit from 2-4 additional weeks of material and more programming assignments, IMO. The network graphing lecture, for example, could have been reinforced with a peer-graded assignment to have us produce 3 or 4 types of graphs of various networks.

Overall, though, I was pleased with this course and the entire specialization. I would definitely recommend it to others.

By John W

•

Jun 11, 2019

This was a good course. I learned a good amount about network analysis and the python library networkx. I can envision using what I learned in my job. However, of the five courses in the Applied Data Science with Python Specialization I felt this was the weakest offering.

1. The Title. While the majority of the examples and exercises were focused on social networks, there's little in the course that is really specific to social networks. The course applies to any kind of network that can be loaded into networkx.

2. Trim the Process Descriptions. Too often the lecturer would say things like "Node A has degree of 3 because it is connected to three other nodes. Node B has a degree of 5 because it is connected to five other nodes. Node C has a degree of 4 because it is connected to four other nodes." For such a simple concept, that many examples aren't needed.

3. Provide On-Screen Example Files (my biggest gripe). In all of the previous courses, when the lecturer gave code examples on screen, there was a corresponding Jupyter notebook with those examples so the learner could follow along, and keep the notebook as a handy refresher of how to interact with the library. None of that was provided in this course.

By PRAGYA P M

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Aug 5, 2023

Great course with lot of interesting concepts laid out very well, complemented well with assignments that strengthen your learning. I, however, had issue with the big data-set in the final assignment. While incorporating most of the concepts that I learned earlier in this course and other courses in the same specialization - cross-validation and hypertuning, it took really long for it to run and didnt even work eventually which was quite frustrating. Had to strip all these techniques to finally receive my answers. I would request you to probably, given this is an online course, provide a smaller data-set [400k+ dataset is just too huge for an online course]

But overall a great course and I enjoyed the lessons!

By Rui B

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Feb 25, 2018

Extremely good introduction to network analysis. The course heavily relies on NetworkX, and doesn't require extensive programming knowledge - with the help of Google, you may easily solve all problems. The lectures were well structured and easy to follow. Having said this, I have found 2 major drawbacks: 1. I would really appreciate some external references so that I could get a theoretical introduction to the materials taught. 2. The last assignment required machine learning, which was not taught in this course. With the help of the forums and a bit of googling, it is easy to get full mark, but perhaps the authors could include such background in the provided notebooks?

By Vinicius G

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Jan 29, 2018

The explanations were very really good and clear but not enough to complete the assignments. The assignments were over the top in difficulty. The hardest in the entire course program. That is the only reason I took one star. It was because I felt that the classes did not prepare for the assignments. Or, assignments should have a more clear explanation of the steps to be taken in order to complete them. Definitely we should look for answers ourselves but not being able to clearly understand each step throughout the assignments really limited my research area and increased my frustration.

By Aino J

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Jul 2, 2020

I started the course only because it was part of the Specialisation, but I am glad I did because the topic is actually very interesting! This course covers the basics. The lectures are very well structured, quizzes are suitably challenging, and the assignments are interesting while not terribly challenging. You'll apply some of the machine learning concepts from course 3 in the final week's assignments, which I though was a nice, round finish to the Specialisation.

By VenusW

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Sep 19, 2017

Learnt considerable amount about social network from this course, as introductory level, materials (lectures and assignments) are well-prepared, much better than course 4 (text-mining). Assignments are not too hard, probably has relative good foundation from previous 4 courses. Auto-grader is a real pain in this specialization (course 3, 4 and 5), need to go through thorough test before release.

Do not consider this specialization as intermediate level.