Prerequisites:
Recommended for those who have completed the 2-part Python Fundamentals series or who have basic Python skills and familiarity with data structures like dictionaries and lists.
Overview:
This workshop introduces the fundamental concepts of network analysis using NetworkX, the standard Python library for the study of networks. Participants will learn how to represent relational data—such as social networks, citation maps, or transport systems—as mathematical graphs to uncover hidden patterns and structures.
The session covers the complete workflow of network analysis, from building a graph to calculating metrics and generating visual representations.
Topics Include:
- Network Basics: Creating nodes and edges, and understanding different graph types (directed vs. undirected).
- Data Import: Loading network data from external files (like CSVs or adjacency lists).
- Network Metrics: Calculating centrality measures (Degree, Betweenness, and Eigenvector) to identify influential nodes.
- Visualization: Using Matplotlib to create customizable network diagrams, including different layouts and attribute-based coloring.
Contact information:
Please email mrsmith@rice.edu if you have questions about the Data@Rice workshop series.
For more information about Python and other data courses at Rice, visit https://library.rice.edu/services/data-workshops, or contact researchdata@rice.edu.
Course Materials: