The module covers the basics of Complexity Science with particular focus on Complex Networks (network science), which are the backbones of complex systems (e.g. cities, organizations, economies, and financial markets). Complex networks quantify the interactions of various entities/players in complex systems. Examples of complex networks include social networks like those generated from Twitter, Facebook, and Instagram, financial networks, biological networks, and organizational networks.
Students learn how to visualize, analyze, and model complex networks using Python, NetworkX, and Gephi. At the end of the course, students should be able to view and analyze problems in business and marketing, among others, through the lens of complexity science. They should also be able to argue, in descriptive and quantitative manner, why a system-of-systems thinking is necessary to address most real-world issues.
The course is capped with a final project.
Also, sharing some essays from my students on their thoughts about and experience in Network Science.