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

2,491 ratings
420 reviews

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

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.

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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376 - 400 of 410 Reviews for Applied Social Network Analysis in Python

By Subeesh P

Nov 9, 2020

Very good experience

By Andrés M A

Mar 7, 2021

Excellent course.

By Nicolas B

Oct 6, 2017

Very good Course.


Oct 16, 2020

nicely explained

By arpit m

Dec 15, 2018

very good course

By Raghunath P

Nov 10, 2018

Great Course!

By Vinit D

Jan 16, 2020

Tough course

By Avi R

Aug 3, 2019


By Jean E K

May 18, 2018

good teacher


Aug 2, 2019

Good course

By Andreas C

Dec 2, 2017

quite good

By Chethan S L

Oct 2, 2019


By Xing W

Dec 3, 2017

Not bad

By shubham z

Jun 13, 2020


By Mallikarjuna R Y

May 5, 2020


By V B

Dec 30, 2020


By Mark H

Feb 7, 2018

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.

By Siddharth S

Jun 14, 2018

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 :)

By Alexandra C

Feb 28, 2021

Videos are very distracting as there are many cutscene from the text to the instructor's face which is very disrupting for the flow of the lecture. Maybe overlaying his face on a small window on the corner will be better

By Daniel B

Dec 18, 2020

This course feels more like an API summary of networkx rather than a real course on social network analysis. On top of that, the course uses the outdated networkx 1.11, while 2.0 has been out for over three years.

By Jeremy .

Jan 1, 2021

Some of the assignment organization could have been better, but otherwise the information was rock solid!

By Jenny z

Dec 1, 2020

better if TA could prepare projects with updated versions of libraries

By József V

May 4, 2018

Useful but weaker comparing to Pandas or Scikit courses.

By Sara C

May 16, 2018

i like the way that lecturer teach.

By Leon V

Oct 8, 2017

it was okay, 3.5 really