VS
4.0Reviewed Jul 15, 2018
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.
Applied Social Network Analysis in Python
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.
Status: Predictive Analytics
Status: Pandas (Python Package)
IntermediateCourse26 hours
Featured reviews
VA
4.0Reviewed Mar 15, 2021
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.
MS
5.0Reviewed Nov 17, 2020
I have never imagined such detailed analysis can be done on a network, nx in python is really powerful package with so many powerful functions that can do ample of analysis at a whim.
PP
5.0Reviewed Apr 24, 2020
Very nice platform to learn & enhance skill. Thanks to Prof. and team.. Also thanks to university and coursera platform for providing such a big platform to us.
SR
5.0Reviewed Mar 27, 2020
Very helpful courses. I was able to review and got much better at some things I already knew like data visualization and was able to explore some new areas like network analysis.
NK
5.0Reviewed 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.
RM
4.0Reviewed Jul 5, 2018
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.
NP
5.0Reviewed Oct 7, 2017
Interesting material and easy to follow. Assignments and quizzes were sufficiently challenging, but not too difficult that I spent entire weekends troubleshooting my code.
JL
5.0Reviewed 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.
CG
5.0Reviewed Sep 17, 2017
Excellent tour through the basic terminology and key metrics of Graphs, with a lot of help from the networkX library that simplifies many, otherwise tough, tasks, calculations and processes.
CA
4.0Reviewed Jan 12, 2018
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.
JA
5.0Reviewed Nov 22, 2020
Great introductory course on graph theory using Networkx. The instructor goes through each algorithm with step-by-step examples, and gives relevant examples at the end of each topic.