Beginning with students admitted in the fall of 2026, all students pursuing on-ground master's degree programs must complete a university-level experiential learning requirement. This requirement may be fulfilled through the curriculum, co-op, or other work- or industry-integrated learning experiences.
Core Requirements
| Code | Title | Hours |
|---|---|---|
| Students must complete 20 semester hours of the core courses listed below, unless the program director approves changes to the student's course plan: | ||
| NETS 5116 | Network Science 1 | 4 |
| NETS 7052 | Computational Methods for Network Science | 4 |
| NETS 7380 | Statistical Methods for Network Science | 4 |
| Students must complete two courses out of the four courses below. Students should consult with their advisor to determine appropriate course selections based on their intended research focus. The remaining courses may be applied toward elective semester hours: | 8 | |
| Network Science 2 | ||
| Machine Learning with Graphs | ||
| Social Networks | ||
| Dynamical Processes in Complex Networks | ||
Electives
| Code | Title | Hours |
|---|---|---|
| Complete 20 semester hours from the elective course options below that have not been used to fulfill a previous requirement. | 20 | |
| The student's elective course plan should be discussed with, and approved by, their advisor. | ||
| Students who seek to apply courses not on this list to fulfill program requirements must obtain written approval from their advisor and the program director. Elective options below reflect domain area research paths such as social networks/computational social science, epidemiology/public health, foundational network science/theory, and computer science/AI/machine learning. | ||
| Network Science Electives | ||
| Network Economics | ||
| Bayesian and Network Statistics | ||
| Research Design for Social Networks | ||
| Computational Urban Science | ||
| Directed Study | ||
| Topics | ||
| Network Science Literature Review Seminar | ||
The following core courses may be used as electives if not already completed to fulfill core requirements of this program: | ||
| Network Science 2 | ||
| Machine Learning with Graphs | ||
| Social Networks | ||
| Dynamical Processes in Complex Networks | ||
| Other Electives | ||
| Students may enroll in the following preapproved electives, which align with program objectives: | ||
| Algorithms | ||
| Natural Language Processing | ||
| Machine Learning | ||
| Data Mining Techniques | ||
| Special Topics in Artificial Intelligence | ||
| Visualization for Network Science | ||
| Special Topics in Data Visualization | ||
| Graph Theory | ||
| Machine Learning and Statistical Learning Theory 1 | ||
| Intermediate Epidemiology | ||
| Principles of Population Health 1 | ||
| Principles of Population Health 2 | ||
| Causal Inference in Public Health Research | ||
| Introduction to Scientific Computing | ||
| Statistical Physics | ||
| Computational Physics | ||
Program Credit/GPA Requirements
40 total semester hours required
Minimum cumulative 3.000 GPA required