We have introduced a two-course sequence on Network Science. The first course, Network Science 201 (NS 201), focuses on fundamental theory and algorithms for working with Big Data and networks. It is taught by the lead faculty in focus areas of Algorithms, Models, and Mining; and Dynamics and Control. Topics covered include embeddings, spanning trees, network flow, random graph models, network formation and evolution, structure and attribute-based search, clustering, partitioning, and distributed dynamical systems. The second course, Network Science 202 (NS 202), explores Network Science concepts in the context of social and biological networks. It is taught by faculty in the focus areas of Networked Users, Content, and Social Organizations, Biological Networks, and Cyberinfrastructure. Topics covered include social structure theory, evolution of biological networks, management of Big Data, and visualization of networks.

Existing courses: A number of existing courses from the participating departments can be used to build a breadth component:

  • CMPSC 211B: Advanced Numerical Simulation
  • CMPSC 240A: Applied Parallel Computing
  • CMPSC 265: Advanced Topics in Machine Intelligence
  • CMPSC 274: Advanced Topics in Database Systems
  • CMPSC 290D: Advanced Topics in Bioinformatics
  • CMPSC 290D: Indexing High-dimensional Data
  • CMPSC 290N: Data Mining
  • ECE 210A: Matrix Analysis and Computation
  • ECE 270: Game Theory
  • ECE 271A: Principles of Optimization
  • EEMB 272: Theoretical Population Ecology
  • EEMB 279: Modeling Environmental and Ecological Change
  • EEMB 280: Evolutionary Theory and Models of Behavioral Processes
  • EEMB508-9: Levels of Biological Organization 1-2
  • ME 225AF: Cooperative Control of Robotic Networks
  • ME 225FB: Distributed Systems and Control
  • PSTAT 213A: Introduction To Probability Theory And Stochastic Processes
  • PSTAT 215A:. Bayesian Inference
  • SOC 203: Logics of Inquiry
  • SOC 207C: History of Network Theory in Social & Natural Sciences
  • SOC 248: Social Network Analysis