Masters in Complex Systems and Data Science

Train to become a protean data scientist with eminently transferable skills, capable of describing, understanding, predicting, and managing complex natural and sociotechnical systems.

Overview

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.

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:

  • describing and understanding complex natural and sociotechnical systems, enabling them to then, as possible,
  • predict, control, manage, and create such systems.

Major Skill Sets

Data Wrangling

Methods of data acquisition, storage, manipulation, and curation.

Visualization

Visualization techniques, with a potential for building high quality web-based applications.

Machine Learning

Uncovering complex patterns and correlations in systems through data-fueled machine learning and genetic programming.

Mechanistic Stories

Powerful ways of identifying and extracting explanatory, mechanistic stories underlying complex systems—not just how to use black box techniques.

Step 1 Prerequisites

Laptop

Students must have prior coursework or competency in:

  • Calculus
  • Coding (Python/R ideal but not necessary)
  • Data structures
  • Linear algebra
  • Probability and Statistics

Catch-up Courses Available

MATH 2522
Applied Linear Algebra
ⓘ
Solving linear systems, vectors, matrices, linear independence, vector spaces, determinants, linear transformations, eigenvalues and eigenvectors, singular value decomposition, and matrix factorizations.
CS 2240
Data Struc & Algorithms
ⓘ
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.
STAT 1410
Basic Statistical Methods 1
ⓘ
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.

These courses cannot be taken for graduate credit.

Step 2 Three Degree Paths

1

Coursework Only

30 credits

2

Coursework and Project

24-27 credits coursework + 3-6 credits project

3

Coursework and Thesis

21-24 credits coursework + 6-9 credits thesis

Step 3 Common Core

9 credits required — Take the first course in each sequence + at least one second course:

Option 1: Data Science

Required

Data Science I: CSYS/CS/STAT 5870

Optional

Data Science 2: CSYS/CS/STAT 6870

Option 2: Modeling

Required

Modeling Complex Systems: CSYS/CS 6020

Optional

Modeling Complex Systems 2

Option 3: Principles

Required

Principles of Complex Systems 1: CSYS/MATH 6701

Optional

Principles of Complex Systems 2: CSYS/MATH 6713

Step 4 Electives

9 credits (3 courses) — Choose from CSDS electives or specialized paths:

View All CSDS Electives
  • 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)

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.

Biomedical Systems
Energy Systems
Environmental Systems
Evolutionary Robotics
Policy Systems
Build-Your-Own-Adventure

Step 5 Travel the Right Path

Path 1: 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 Elective Paths below.
Path 2: 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 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.

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.

Path 3: 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 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.

Finding a Faculty Advisor

In your first semester after admission, if 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.

Step 6 Optional Elective Paths

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

Build-Your-Own-Adventure

Design your own path with your advisor.

Biomedical Systems

Domain Consultant: Jason Bates

Energy Systems

Domain Consultant: Mads Almassalkhi

Environmental Systems

Domain Consultants: Donna Rizzo and Taylor Ricketts

Evolutionary Robotics

Domain Consultant: Josh Bongard

Policy Systems

Domain Consultant: Asim Zia

How to Apply

Deadline
February 15
GRE
Not Required
TOEFL
90 minimum (100 for TA funding)
Duration
2 years standalone
1 year accelerated (UVM undergrads)
Program Director
Professor Laurent Hébert-Dufresne
Apply
UVM Graduate College