Admissions Requirements:
Students must have prior coursework or be able to establish competency in:
- Calculus,
- Computer programming,
- Data structures,
- Linear algebra,
- Probability and Statistics.
We offer three courses for students who may be lacking in these prerequisites:
- CS 124 Data Structures,
- MATH 122 Applied Linear Algebra, and
- STAT 211 Statistical Methods I.
Note that at most one of CS 124 or Math 122 may be taken for graduate credit, and the student
must submit a form for pre-approval from the Graduate College at least 1 month before the semester in which they take the course.
Course descriptions:
CS 124 Data Structures: Design and implementation of linear structures, trees and graphs.
Examples of common algorithmic paradigms. Theoretical and empirical complexity analysis.
Sorting, searching, and basic graph algorithms.
Math 122 Applied Linear Algebra: Solving linear systems, vectors, matrices, linear
independence, vector spaces, determinants, linear transformations, eigenvalues and
eigenvectors, singular value decomposition, and matrix factorizations.
STAT 211 Statistical Methods I: Fundamental concepts for data analysis and experimental
design. Descriptive and inferential statistics, including classical and nonparametric methods,
regression, correlation, and analysis of variance. Statistical software.
No GRE Required
For International Students: TOEFL score requirements.
Minimum for admission to the Graduate College at UVM: 90.
Minimum for a student to qualify for funding at UVM: 100.
Program Requirements:
The degree program can be completed with one of three options:
- Coursework only
- Coursework and project
- Coursework and thesis
12 credits: Common Core (4 courses):
- 3 credits: CSYS/Math 300 Principles of Complex Systems.
- 3 credits: CSYS/CS 302 Modeling Complex Systems.
- 3 credits: Stat 287 Data Science I.
- 3 credits: Stat 387 Data Science II.
comprehensive exam requirement:
Receiving an A- or above in at least two of the four core courses and a B or above in the other two core courses meets the comprehensive exam requirement. If students do not meet this standard, they must demonstrate mastery of the material in which they have not proved to have satisfactory knowledge by one of three possible routes: an oral exam, a written exam, or a paper. The exact format will be decided upon by the Curriculum Committee in consultation with the student. The Curriculum Committee will also designate three relevant faculty who will create the exam and or specify the format and content area of the paper and assess the student’s performance.
9 Credits: Complex Systems and/or Data Science electives (3 courses)
- CSYS/Math 266 Chaos, Fractals and Dynamical Systems
- CSYS/Math 303 Complex Networks
- CSYS/CS/Biol 352 Evolutionary Computation
- CSYS/CE 359 Applied Artificial Neural Networks
- CSYS/STAT/CE 369 Applied Geostatistics
- CS 204 Database Systems
- CS 228 Human Computer Interaction
- CS 254 Machine Learning
- CS 332 Data Mining
- STAT 221 Statistical Methods II
- STAT 223 Multivariate Analysis
- STAT 224 Stats for Quality & Productivity
- STAT 225 Applied Regression Analysis
- STAT 229 Logistic Regression and Survival Analysis
- STAT 231 Experimental Design
- STAT 251 Probability Theory
- STAT 235 Categorical Data Analysis
- STAT 241 Statistical Inference
- STAT 251 Probability Theory
- STAT 261 Statistical Theory
- STAT 330 Bayesian Statistics
- STAT/CS 295 Statistical Learning
- Other advanced Complex Systems and Data Science electives approved by the MS in CSDS Curriculum Committee (including special topics)
9 Credits: Elective Paths (3 courses)
Coursework Only Option:
Students choosing a coursework option must complete a minimum of 30 credit hours of
coursework. They will need 9 credits of either an elective path or additional Complex Systems
and Data Science courses.
Either:
CSDS: Pure CSDS Path – Three (3) additional Complex Systems and/or Data Science Electives
from the list above.
Or:
Three (3) courses in one of the following Elective Paths:
Biomedical Systems (domain consultants: Jason Bates)
- CSYS/ME 312 Advanced Bioengineering Systems
- CSYS/ME 350 Multi-Scale Modeling
- CS/MMG 232 Methods in Bioinformatics
- CTS 302 Quality in Health Care
- CSYS/MATH 268 Mathematical Biology & Ecology
- STAT/BIOS/MPBP 308 Biometrics & Applied Statistics
- STAT/BIOS 350 Advanced methods in biostatistics
- Other approved advanced electives in biomedical systems related areas
Distributed Systems (domain consultant: Chris Skalka)
- CS 265 Computer Networks
- CS 266 Network Security & Cryptography
- CS 275 Mobile Apps and Wireless Devices
- CS 361 Wireless Sensor Network Applications
- Other approved advanced electives in distributed systems
Energy Systems (domain consultants: Paul Hines, Mads Almassalkhi)
- EE 215 Electric Energy Systems Analysis
- EE 217 Smart Grid
- CE 295 Reliability of Engineering Systems
- EE 395 Optimization in Engineering
- Other approved advanced electives related to energy
Environmental Systems (domain consultants: Donna Rizzo and Taylor Ricketts)
- CSYS/STAT/CE 369 Applied Geostatistics
- ENVS 295 Environmental Modeling and Systems Thinking
- Geog 281 Advanced Topic: GIS & Remote Sensing
- Geog 287 Spatial Analysis
- NR 245 Integrating GIS & Statistics
- NR 343 Fundamentals of Geographic Information Systems
- Other approved advanced electives related to the environment
Evolutionary Robotics (domain consultant: Josh Bongard)
- CS 206 Evolutionary Robotics
- CSYS/CS 352 Evolutionary Computation
- CSYS/CE 359 Applied Artificial Neural Networks
- Biol 271 Evolution
- ME 338: Advanced Dynamics
- Other approved advanced electives related to evolutionary robotics
Policy Systems (domain consultants: Chris Koliba and Asim Zia)
- PA 306 Policy Systems
- PA 308 Decision Making Models
- PA 311 Policy Analysis
- PA 317 Systems Analysis and Strategic Management
- PA 395 Resilient Communities: Designing at the Nexus of Food, Energy and Water Systems
- PSYC 296 Behavioral Economics
- Other approved advanced electives related to policy
CSDS: Self-designed named disciplinary path (requires approval of the CSDS advisor)
Note: We have identified and engaged with Domain Consultants to generate the elective course
lists above. The Curriculum Committee will ensure that the path options remain current, and
will communicate with Domain Consultants both annually and on an as-need basis.
Coursework and Project Option:
Students must complete a minimum of 30 credit hours, comprising 24 to 27 credits of
coursework and 3 to 6 credits of project (CSYS 392).
A graduate project typically consists of a significant study of a data-rich problem carried out
under the supervision of a faculty member. Full-time students should plan to search for and
acquire a project advisor by the end of their first semester.
The results of the project must be presented before a project committee in a public talk, which
has been advertised to the community. The project committee must include two or three
individuals. The chair, who may be the project advisor, must be a member of the Graduate
College. The composition of the committee must be approved by the Curriculum Committee. A
pdf (or similar) of the report along with accompanying web products should be submitted to
the Graduate Program Coordinator within 30 days after the defense. The products will be
housed online by the Vermont Complex Systems Center.
Coursework and Thesis Option:
Students choosing the thesis option must complete a minimum of 30 credit hours, including 21
to 24 credits of coursework and 6 to 9 credits of thesis research (CSYS 391).
A Master’s thesis consists of original research work done under the guidance of a faculty
member. Students opting to pursue a thesis must find and arrange a thesis advisor in their first
semester.
The student must defend their thesis before committee in a public oral thesis defense. The
thesis committee must include three members of the Graduate College and include the thesis
advisor.
At least three weeks before the defense, the written thesis must be submitted to the Graduate
College for a format check. At least two weeks before the defense, the student must make
electronic copies of the written thesis available to all members of the thesis committee. The
thesis defense itself must be adequately advertised to the community.
Core participating units at UVM:
- College of Engineering and Mathematical Sciences (CEMS).
- Department of Mathematics and Statistics.
- Department of Computer Science.
- School of Engineering.
Optional tracks involve courses the following UVM colleges:
- College of Arts and Sciences (CAS).
- College of Agriculture and Life Sciences (CALS).
- Rubenstein School for Environment and Natural Resources (RSENR).
- College of Medicine (CoM).
Mission:
Our central goal is to help students become
protean data scientists with eminently transferable skills (read: super powers).
Our educational foundation:
The MS in CSDS is a natural expansion of
our successful five course
Graduate Certificate in Complex Systems
which trains students from
all disciplines to tackle data-rich problems.
Program director:
Prof. Peter Dodds, Director of the Vermont Complex Systems Center.
Contact information:
Please direct inquiries to
Andrea Elledge,
Associate Director of Operations and Outreach.
