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Masters in Complex Systems and Data Science

Our Masters in Complex Systems and Data Science (CSDS) trains emerging data scientists to find, model, understand, and tell the stories of the patterns they uncover.

Our coursework comprises a balanced core of Complex Systems and Data Science and includes choose-your-own adventure options.

The Masters may be earned as a two year stand-alone degree or in one year as part of an Accelerated Masters for UVM undergraduate students.

Application Basics:

Application deadline Feb 1

International students will need to apply well in advance taking into consideration visa processes.

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When you're ready, please apply online through UVM's Graduate College.

Program director: Prof. Peter Dodds, Director of the Vermont Complex Systems Center.

Educational Mission:

Our Essential Goal:

We enable students to become protean data scientists with eminently transferable skills (read: super powers).

Our More Detailed Goal:

We provide students with a broad training in computational and theoretical techniques for (1) describing and understanding complex natural and sociotechnical systems, enabling them to then, as possible, (2) predict, control, manage, and create such systems.


Our Masters is a natural expansion of our five course Graduate Certificate in Complex Systems.


The Curious Platypus

Major skill sets we want students at all levels to develop:

  1. Data wrangling: Methods of data acquisition, storage, manipulation, and curation.
  2. Visualization techniques, with a potential for building high quality web-based applications.
  3. Uncovering complex patterns and correlations in systems through data-fueled machine learning, and genetic programming.
  4. Powerful ways of identifying and extracting explanatory, mechanistic stories underlying complex systems—not just how to use black box techniques.

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Make your undegraduate degree go "voom":

The Curious Platypus

Accelerated Master's Degree Program:

Undergraduates at the University of Vermont may incorporate the degree as part of an Accelerated Master's Program (4 + 1 years).

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Six Steps to Obtaining Foxfulness:

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1 of 6: The academic background you'll need

Students must have prior coursework or be able to establish competency in:

  1. Calculus
  2. Coding (Python/R ideal but not necessary)
  3. Data structures
  4. Linear algebra
  5. Probability and Statistics

The Curious Platypus

We offer three catch-up courses for students who are missing these prerequisites:

  1. Applied Linear Algebra (MATH 122)
  2. Data Structures (CS 124)
  3. Statistical Methods I (STAT 211)

But not all three courses can be taken together:

At most one of MATH 122 or CS 124 may be taken for graduate credit. Students must also submit a form for pre-approval from the Graduate College at least 1 month before the semester in which they take the course.

Catch-up course descriptions

Applied Linear Algebra (MATH 122):

Solving linear systems, vectors, matrices, linear independence, vector spaces, determinants, linear transformations, eigenvalues and eigenvectors, singular value decomposition, and matrix factorizations.

Data Structures (CS 124):

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.

Statistical Methods I (STAT 211):

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.

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Extra pieces for Admission:


No GRE (or Jacket) Required.

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International Students:

TOEFL score thresholds:

Minimum for admission: 90.
Minimum to qualify for funding in a teaching assistant position at UVM: 100.

Once you've joined the team …

Time to Get with the Program:

2 of 6: Choose one of Three Major Paths:

1. Coursework Only

2. Coursework and Project
3. Coursework and Thesis

(Details below.)

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3 of 6: Travelers of All Paths must take the Four Course Common Core (12 credits):

Principles of Complex Systems

Modeling Complex Systems
(CSYS/CS 302)

Data Science I
(STAT/CS 287)

Data Science II
(STAT/CS 387)

(Each course scores 3 credits.)

4 of 6: All Travellers must also choose 3 elective courses (9 credits):

  • Complex Systems and Data Science Electives:
    • Chaos, Fractals and Dynamical Systems (CSYS/MATH 266)
    • Complex Networks (CSYS/MATH 303)
    • Evolutionary Computation (CSYS/CS/BIOL 352)
    • Applied Artificial Neural Networks (CSYS/CE 359)
    • Applied Geostatistics (CSYS/STAT/CE 369)
    • Database Systems (CS 204)
    • Human Computer Interaction (CS 228)
    • Machine Learning (CS 254)
    • Data Mining (CS 332)
    • Statistical Methods II (STAT 221)
    • Multivariate Analysis (STAT 223)
    • Stats for Quality & Productivity (STAT 224)
    • Applied Regression Analysis (STAT 225)
    • Logistic Regression and Survival Analysis (STAT 229)
    • Experimental Design (STAT 231)
    • Probability Theory (STAT 251)
    • Categorical Data Analysis (STAT 235)
    • Statistical Inference (STAT 241)
    • Probability Theory (STAT 251)
    • Statistical Theory (STAT 261)
    • Bayesian Statistics (STAT 330)
    • Statistical Learning (STAT/CS 295)

Two things:

This course list evolves and not all courses will be offered in any given semester.
Other courses (including special topics) may be approved by the CSDS Curriculum Committee.

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5 of 6: Travel the Right Path

Click on the Correct Button for Illumination:

  • Coursework Only

    Students must complete a minimum of 30 credit hours and they can:

    Either take the pure CSDS Path and choose three (3) or more Complex Systems and Data Science Electives from the list above.

    Or choose three (3) or more courses in one of the following Elective Paths below.

  • Coursework and Project

    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

    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.

    Students are responsible for checking with the graduate college, one year before planned graduation, about relevant forms and procedure for preparing and defending their thesis

  • Faculty Advisor

    In your first semester after admission, and you wish to pursue the project or thesis option, please identify a faculty advisor.

    If you do not recruit a faculty advisor, you will have to follow the coursework only track.

    You identify a faculty advisor by meeting with faculty.

    After identifying an advisor, please obtain written consent and ask the advisor to email the graduate coordinator.

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Optional Elective Paths to Tailor your Masters

Instead of choosing 3 more pure CSDS courses, here are some other directions:


Design your own path with your advisor.

  • Energy Systems
    Domain consultants: Paul Hines and Mads Almassalkh
    • 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
  • 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
  • Biomedical Systems
    Domain consultant: 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
  • 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
  • Policy Systems
    Domain consultant: 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
  • 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

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6 of 6: Contend with the Comprehensive Exam

Win Condition A:

Receive 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.

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Win Condition B:

If students do not meet the standard of Win Condition A, they must demonstrate mastery of the material by one of three possible routes: an oral exam, a written exam, or a paper. The exact format will be decided upon by the CSDS Curriculum Committee in consultation with the student. The CSDS 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.

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