Certificate of Graduate Study in Complex Systems and Data Science
Program Overview
Certificate of Graduate Study in Complex Systems and Data Science
The Certificate of Graduate Study in Complex Systems and Data Science is a post-baccalaureate graduate certificate program offered by the College of Engineering and Mathematical Sciences at the University of Vermont.
Program Overview
This certificate program provides students with a broad training in computation and theoretical techniques for describing and understanding complex natural and sociotechnical systems. The program enables students to predict, control, manage, and create such systems. Students will learn data wrangling, visualization techniques, uncovering complex patterns and correlations in systems, and identifying explanatory stories underlying complex systems.
Curriculum
The Certificate requirement is 5 courses (15 credits), with a minimum GPA of 3.0 in all 5 courses.
Structure
- 3 required core courses
- 1-2 A-list courses
- 0-2 B-list courses
Required Core Courses
- CSYS/MATH 6701: Principles of Complex Systems
- CSYS/CS 6020: Modeling Complex Systems
- CSYS/STAT/CS 3870: QR: Data Science I
A-List Courses
- CSYS 5766: Chaos, Fractals, and Dynamical Systems
- CSYS/MATH 6713: Complex Networks
- CSYS/CS 6520: Evolutionary Computation
- CSYS/CS 3560: Neural Computation
- CSYS/STAT 5530: Appl Time Series & Forecasting
- CSYS/STAT/CEE 7980: Applied Geostatistics
- CSYS/CE 7920: Applied Artificial Neural Networks
B-List Courses
- CSYS/CS/STAT 6870: Data Science II
- MATH 5788: Mathematical Biology & Ecology
- MATH 5230: Adv. Ordinary Differential Equations
- CEE 3990: Reliability of Engineering Systems
- CEE 6990A: Data Analytics for Water Resources
- CSYS/ME 3990: Systems and Synthetic Biology
- ME 5410: Advanced Bioengineering Systems
- EE 5320: Smart Grid
- ME 6550: Multi-Scale Modeling
- CSYS/EE 6990: Optimization in Engineering
- PA 6080: Decision Making Models
- PA 6170: Systems Analysis and Strategic Management
- PA 6060: Policy Systems
- BIOL 3165: Evolution
- PBIO 5940: Ecological Modeling
- CS 3060: Evolutionary Robotics
- CS 3540: Machine Learning
- CS 6540: Deep Learning
- ENVS 4990: Envir. Modeling and Systems Thinking
- NR 385: Energy Systems Transitions
- PHYS 323: Phase Transitions and Critical Phenomena
Deadlines
- Fall admittance: Application deadline is August 1
- Spring admittance: Application deadline is December 31
Admissions
The certificate can be earned by students as a complement to their graduate degrees across the University of Vermont, or as a standalone post-baccalaureate graduate certificate.
Prerequisites
- A Bachelor's degree and demonstrated proficiency in:
- Calculus
- Probability and statistics
- Computer programming (Python, R, and Matlab will be most helpful)
- Highly recommended (but not required):
- Linear algebra
Faculty
Professor Peter Dodds is the Certificate's Program Graduate Coordinator.
Costs and Funding
Tuition
Information associated with the Certificate of Graduate Study in Complex Systems can be found at the Student Financial Services site.
Financial Aid
Federal or institutional financial aid is not available for students enrolled solely in a certificate program. Student Financial Services has details about other potential financing options for the certificate program.
Frequently Asked Questions
Is it possible to take classes and work full-time?
Yes. Students should plan to spend 5 to 20 hours per week on each class.
Can I transfer my credits from my Complex Systems and Data Science Certificate to a Master's program?
Credits used for a Certificate of Graduate Study may be applied toward an appropriate master's or doctoral degree at UVM.
Is financial aid available for this program?
Federal or institutional financial aid is not available for students enrolled solely in a certificate program. The Student Financial Services website includes details about other potential financing options for the certificate program.
What are the prerequisites for the Certificate?
A Bachelor's degree and demonstrated proficiency in calculus, probability and statistics, and computer programming. Highly recommended (but not required) is linear algebra.