The course is an introduction to Network Science, which includes identifying complex networks, describing their structure through exploratory data analysis, and understanding their dynamics given their topology and structural properties. The course also provides a concise introduction to Complex Systems, particularly highlighting the connection of the field to Network Science. Discussions will involve real-world complex networks with examples from social science/economics, finance, epidemiology, engineering, and urban science, among others.

Students will learn how to identify complex systems, represent them as complex networks, and then visualize, analyze, and model these networks. We will use the NetworkX Python library extensively in this course; Gephi and graph-tool will also be introduced. At the end of the course, students should be able to view and analyze problems involving policymaking, business process analysis, and even marketing, among others, through the lens of Network Science and Complexity Science. They should also be able to argue, descriptively and quantitatively, why system-of-systems thinking, with a particular focus on interactions, is necessary to address most real-world issues.