Data Science (MS)

Program Policy Document:

Please see the Program Policy Document for more information.  

Requirements for the Degree:

A total of 30 credits is required for the degree. If the student lacks background knowledge for one or more courses, prerequisite courses may need to be taken that do not count toward the degree.

Core Requirements:

18 credits in total from the four foundations listed below as well as the Ethics Course. Minimum grade of B- required for all core courses.

Electives:

Twelve (12) credits of elective courses may come from a variety of courses on campus with relevant application or quantitative content. A list of example courses is below. The elective list is not meant to be exhaustive. The courses must be at the 600 level or above. A course from the core lists may be chosen as an elective provided that it has not already been used to satisfy the core course requirement. The electives taken by the student must be approved by the advisor and the Assistant Director of the MSDS prior to registration.

• ACCT 602 - Big Data Technologies Credit(s): 3
• BINF 620 - Big Data Analytics in Healthcare Credit(s): 3
• BINF 644 - Bioinformatics Credit(s): 3
• BINF 694 - Systems Biology I Credit(s): 3
• BINF 695 - Computational System Biology Credit(s): 3
• CHEG 848 - Computing and Data Science for Soft Materials Innovation Credit(s): 3
• CIEG 652 - Transportation Facilities Design Credit(s): 3
• CIEG 654 - Urban Transportation Planning Credit(s): 3
• CIEG 655 - Civil Infrastructure Systems Credit(s): 3
• CISC 612 - Software Design Credit(s): 3
• CISC 636 - Computational Biology and Bioinformatics Credit(s): 3
• CISC 642 - Introduction to Computer Vision Credit(s): 3
• CISC 665 - Introduction to Cybersecurity Credit(s): 3
• CISC 681 - Artificial Intelligence Credit(s): 3
• CISC 682 - Introduction to Human-Computer Interaction Credit(s): 3
• CISC 688 - Introduction to Natural Language Processing Credit(s): 3
• CISC 689 - Topics: Artificial Intelligence Credit(s): 3-12
• CISC 889 - Advanced Topics in Artificial Intelligence Credit(s): 3
• DASC 864 - Internship Credit(s): 1-6
• DASC 866 - Special Problem Credit(s): 1-12
• DASC 868 - Research Credit(s): 1-12
• EDUC 862 - Randomized Trials in Education Credit(s): 3
• EDUC 865 - Educational Measurement Theory Credit(s): 3
• EDUC 871 - Advanced Structural Equation Modeling Credit(s): 3
• EDUC 872 - Advanced Educational Measurement Credit(s): 3
• EDUC 873 - Multilevel Models in Education Credit(s): 3
• ELEG 604 - Imaging and Deep Learning Credit(s): 3
• ELEG 605 - Engineering Machine Learning Systems Credit(s): 3
• ELEG 630 - Information Theory Credit(s): 3
• ELEG 817 - Large Scale Machine Learning Credit(s): 3
• ELEG 845 - Modern Machine Learning Credit(s): 3
• GEOG 670 - Geographic Information Systems and Science Credit(s): 3
• GEOG 671 - Advanced Geographic Information Systems Credit(s): 3
• GEOG 681 - Remote Sensing of Environment Credit(s): 3
• LING 890 - Studies in Linguistics Credit(s): 3-9
• MATH 611 - Introduction to Numerical Discretization Credit(s): 3
• MATH 620 - Introduction to Mathematical Finance Credit(s): 3
• MATH 631 - Introduction to Stochastic Processes Credit(s): 3
• MEEG 621 - Linear Systems Credit(s): 3
• MEEG 677 - Introduction to State Estimation Credit(s): 3
• MEEG 877 - Sensing and Estimation in Robotics Credit(s): 3
• MEEG 890 - Nonlinear Programming Credit(s): 3
• MISY 615 - Enterprise Architecture and Business Strategy Credit(s): 3
• MISY 636 - Unstructured Data Analytics Credit(s): 3
• PHYS 607 - Mathematical and Computer Algebra Methods in Physics Credit(s): 3
• PHYS 616 - Statistical Physics and Thermodynamics Credit(s): 3
• PHYS 650 - Introduction to Quantum Computation and Quantum Information Credit(s): 3
• PHYS 660 - Computational Methods of Physics Credit(s): 3
• PHYS 664 - Machine Learning for Time Series Analysis Credit(s): 3
• SPPA 720 - Principles of Urban Data Science Credit(s): 3
• SPPA 721 - Data Science Tools for Evidence-based Policy Credit(s): 3
• STAT 602 - Mathematical Statistics Credit(s): 3
• STAT 608 - Statistical Research Methods Credit(s): 3
• STAT 612 - Advanced Regression Techniques Credit(s): 3
• STAT 619 - Time Series Analysis Credit(s): 3
• STAT 622 - Statistical Network Analysis Credit(s): 3
• STAT 656 - Biostatistics Credit(s): 3
• STAT 675 - Logistic Regression Credit(s): 3
• UAPP 689 - Innovation and Technology in Public and Nonprofit Organizations Credit(s): 3

Non-thesis option:

Up to six (6) credits total from Internship (DASC 864), Special Problem (DASC 866), or Research (DASC 868) can be applied toward the elective credit total. These credits must be related to the degree. Valid scholarly output from such credits are presentations (oral or poster), papers, reports or similar products that demonstrate related work in the field. These elective credits must be approved by the advisor and the Associate Director or Director by no later than the end of the free drop/add period of the semester in which the credits are taken.

Thesis option:

If the thesis option is elected, a minimum of six (6) credits of DASC 869 are required. Six credits of DASC 869 may be applied toward the total elective credit requirement, in which case no credits from DASC 864, DASC 866, or DASC 868 may be applied toward the requirement. All University requirements and deadlines for master’s theses shall apply to the thesis in this degree. The committee for the thesis shall consist of three members, with at least one whose home department is outside of the department of the advisor. An oral defense of the thesis is required. The thesis committee oversees and evaluates the defense, which must be open to the academic community.

• DASC 869 - Master’s Thesis Credit(s): 1-6

Credits to Total a Minimum of 30

Last Revised for 2025-2026 Academic Year