Data Science (MS)

Program Educational Goals

Educational Goals

Demonstrate a mastery of foundational knowledge in data science through the successful completion of a diverse range of coursework encompassing multiple areas of domain and technical expertise.
Develop and apply data science techniques in one or more domain areas of interest via course projects, examinations, and/or a master’s thesis.
Communicate data science techniques and findings to expert and non-expert audiences in written, discussion, visual, and oral presentation formats.
Demonstrate a deep understanding of ethics in data science and related technical tools through in-depth discussion and study of current ethical issues in the field.
Gain experiential training-via on-campus projects or external internships-in workflow management, technical collaboration, and professional accountability in preparation for the expectations of the workplace.

Program Policy Document:

Please see the Program Policy Document for more information.

Requirements for the Degree:

A total of 33 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.

Probabilistic and Statistical Foundations:

Choose at least two of the following (6 credits):

STAT 611 - Regression Analysis Credit(s): 3
STAT 613 - Applied Multivariate Statistics Credit(s): 3
EDUC 812 - Regression and Structural Equation Modeling Credit(s): 3
EDUC 874 - Applied Multivariate Data Analysis Credit(s): 3
MATH 630 - Probability Theory and Applications Credit(s): 3

Databases and Data Mining Foundations:

Choose at least one of the following (3 credits):

CISC 637 - Database Systems Credit(s): 3
CISC 683 - Introduction to Data Mining Credit(s): 3
STAT 674 - Applied Data Base Management Credit(s): 3
CPEG 657 - Search and Data Mining Credit(s): 3
EDUC 875 - Educational Data Mining Credit(s): 3

Machine Learning Foundations:

Choose at least one of the following (3 credits):

CISC 684 - Introduction to Machine Learning Credit(s): 3
ELEG 815 - Analytics I: Statistical Learning Credit(s): 3
MATH 637 - Mathematical Techniques in Data Science Credit(s): 3
PHYS 661 - Data Science for Physical Scientists Credit(s): 3
SPPA 722 - Machine Learning for Public Policy Credit(s): 3
STAT 617 - Multivariate Methods and Statistical Learning Credit(s): 3

Mathematical and Computational Foundations:

Choose at least one of the following (3 credits):

CISC 621 - Algorithm Design and Analysis Credit(s): 3
MATH 612 - Computational Methods for Equation Solving and Function Min Credit(s): 3
STAT 603 - Statistical Computing and Optimization Credit(s): 3

Note:

*Only one of STAT 674 and CISC 683 may be taken for credit toward the degree.

* Only two of these courses may be applied as required courses toward the degree - ELEG815, STAT617, MATH637, SPPA722, PHYS661.

* Only one of MATH 612 and STAT 603 may be applied as required courses toward the degree.

Ethics:

A three-credit ethics course on data science is required.

PHIL 655 - Ethics in Data Science and Artificial Intelligence Credit(s): 1-3

Electives:

Fifteen (15) credits of elective courses may come from a variety of courses on campus with 16 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.

BINF 644 - Bioinformatics Credit(s): 3
BINF 694 - Systems Biology I Credit(s): 3
BINF 695 - Computational System Biology Credit(s): 3
CIEG 642 - Advanced Data Analysis 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 882 - 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
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 873 - Multilevel Models in Education 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 673 - Select Technical Topics Credit(s): 1-3
GEOG 681 - Remote Sensing of Environment Credit(s): 3
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
PHYS 607 - Mathematical and Computer Algebra Methods in Physics Credit(s): 3
PHYS 616 - Statistical Physics and Thermodynamics Credit(s): 3
PHYS 660 - Computational Methods of Physics Credit(s): 3
PHYS 664 - Machine Learning for Time Series Analysis Credit(s): 3
SPPA 721 - Data Science Tools for Evidence-based Policy Credit(s): 3
STAT 602 - Mathematical Statistics 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 684 - Performance Management and Program Evaluation Credit(s): 3
UAPP 689 - Innovation and Technology in Public and Nonprofit Organizations Credit(s): 3
UAPP 701 - Public Policymaking Credit(s): 3

Non-thesis option:

33 credits of course work are required. Up to three credits of Special Problem or Research can be applied toward the credit total. These Special Problem or Research credits may come from experience on campus or in industry (e.g., internships). Special Problem or Research credits must be related to the degree and must be approved by the advisor and the executive committee. Valid scholarly output from such credits are presentations (oral or poster), papers, reports or similar that demonstrate related work in the field.

Thesis option:

A minimum of six credits must be taken to do a MS thesis. The University requirements for master’s theses shall apply to the thesis in this degree. The committee for the thesis shall include three members with at least one member not from the department of the advisor.

Data Science (MS)

Program Educational Goals

Program Policy Document:

Requirements for the Degree:

Core Requirements:

Probabilistic and Statistical Foundations:

Databases and Data Mining Foundations:

Machine Learning Foundations:

Mathematical and Computational Foundations:

Note:

Ethics:

Electives:

Non-thesis option:

Thesis option:

Last Revised for 2023-2024 Academic Year