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Learner Reviews & Feedback for Applied Social Network Analysis in Python by University of Michigan
2,722 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
CG
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.
JA
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.
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