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 15

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

More Information

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

Program director: Professor Laurent Hébert-Dufresne

Apply Now

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.

Foundation

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

#scaffolding


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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.

Make your undergraduate degree go “voom”

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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).

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

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.


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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

GRE?

No GRE (or Jacket) Required.

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 forthcoming below.)


3 of 6: Travelers of All Paths must take the first course in each sequence in the Common Core (9 credits) and at least one accompanying second course:

Common Core:

First Sequence
  1. Data Science I: CSYS/CS/STAT 5870 (required)
  2. Data Science 2: CSYS/CS/STAT 6870
Second sequence
  1. Modeling Complex Systems: CSYS/CS 6020 (required)
  2. Modeling Complex Systems 2: Course Number TBA
Third Sequence
  1. Principles of Complex Systems 1: CSYS/MATH 6701 (required)
  2. Principles of Complex Systems 2: CSYS/MATH 6713

(Each course scores 3 credits.)


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

  • Chaos, Fractals and Dynamical Systems (CSYS 5766)
  • Complex Networks (CSYS/MATH 6713)
  • Evolutionary Computation (CSYS/CS 6520)
  • Applied Artificial Neural Networks (CSYS/CEE 7920)
  • Applied Geostatistics (CSYS/STAT/CEE 7980)
  • Database Systems (CS 3040)
  • Human Computer Interaction (CS 3280)
  • Machine Learning (CS 3540)
  • Statistical Methods II (STAT 3210)
  • Multivariate Analysis (STAT 5230)
  • Logistic Regression and Survival Analysis (STAT 5290)
  • Experimental Design (STAT 5310)
  • Categorical Data Analysis (STAT 5350)
  • Probability Theory (STAT 5510)
  • Statistical Theory (STAT 5610)
  • Bayesian Statistics (STAT 6300)
  • Statistical Learning (STAT/CS 3990)

Two Things:

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

5 of 6: Travel the right path

Click on the correct dropdown for illumination:

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.

Students must complete a minimum of 30 credit hours, comprising 24 to 27 credits of coursework and 3 to 6 credits of project (CSYS 6392).

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.

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 6391). 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.

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:
Build-Your-Own-Adventure

Design your own path with your advisor.

Domain Consultant: Jason Bates

Domain Consultant: Mads Almassalkhi

Domain Consultants: Donna Rizzo and Taylor Ricketts

Domain Consultant: Josh Bongard

Domain Consultant: Asim Zia