Applied Social Network Analysis in Python

4) More time should have been spent on prediction and other advanced topics, at least another week to bring the "Applied" into "Applied Social Network Analysis.

very good introductory course for social network analysis using Python.

The instructor was very clear in what he presented, and gave a good overview of Social Network Analysis.

老师讲解的非常好 ， 逻辑清楚，条理明晰。建议编程作业稍微有点难度。所以扣掉一颗星。 希望越来越好。 Nice overview of general graph theory, and some useful exercises on how it can be applied for social network analysis.

the very best course it is very helpful and useful Excellent Good starting point for those who want ro learn social network analysis.

One of the best courses on social network analysis.

I found it hard sometimes to understand the concepts but this gave me quite an introduction on social network analysis and encouraged me to learn more about them.

ok Good Course This course is a excellent introduction to social network analysis.

Great hands on learning experience to social network analysis in Python 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 A very interesting course, beyond my expectation.

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 :) Very good insights into social network analysis.

The last assignment required machine learning, which was not taught in this course.

Thanks!The cherry on top was to apply machine learning techniques to predict how the net evolves.

Final programming assignment was very easy, you can re-use the code written in the final assignment of Machine Learning course in this specialisation (but that does not mean it's a bad thing).

The machine learning connection could have been mentioned earlier in the course Very helpful, I didn't know anything about graphs, networks modelling and the NetworkX package before this course.

Anyone learning Machine Learning and AI should definitely take this course.

Brought together several machine learning and python skills that I learned in the previous courses.

Very new on this topic and very interesting It was a wonderful course, linked network's models and machine learning.

I was really satisfied from the last week assignment when I had to work with real-life example plus machine learning classifier.

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

It provides a brief but comprehensive introduction to network analysis.

This class was an excellent introduction to network analysis, where concepts, metrics and purpose of application where provided in a clear and digestible manners.

A bit intense, bu rewarding 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.

The last assignment was challenging enough to bring the entire specialization to to satisfying close.

The last assignment was specially fun.

The assignments were not as difficult as in other courses of the specialization, and very helpful to understand the contents.

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.

Personally I thought it was pitched at just the right level because the ML work is just enough to have to go through the process, without any complicated feature optimisation.Only wish the other courses worked as well as this one.

I particularly liked the final assignment.