M.S. Plan C Curriculum
Starting in Spring 2026, the Data Science MS will have a Plan C option available to all students. Students admitted to the Data Science MS program prior to Spring 2026 who would like to switch to the Plan C option will need to contact the Graduate Program Coordinator at [email protected].
Plan C is the coursework-only track where students spend the duration of their time attending class to complete the degree. No committee or advisor of record is required for this plan.
To satisfy all program requirements, admitted students must complete the following courses:
- Statistics track credits - 6 credits
- Algorithmics track credits - 6 credits
- Infrastructure and large scale computing track credits - 6 credits
- Colloquium credits - 1 credit
One credit of the Data Science Colloquium (or equivalent in a participating department) is mandatory and must appear on the student’s graduate degree plan form. - Additional elective credits to reach at least 31 credits
Total credits for the degree - 31 credits
*8xxx-level course requirement - students must complete one, 3-credit 8xxx-level course as a part of the 31 credit requirement - 3 credits
Graduation steps can be found on the Degree Completion Steps Checklist.
Courses
- Statistics
- Algorithmics
- Infrastructure & Large Scale Computing
- 8xxx Level Course Requirement
- Electives
- Colloquium
Take two courses (totaling 6-8 credits) from the following list of courses, at least one of which is a Tier I course:
Tier I courses
- STAT 5101 - Theory of Statistics I / MATH 5651 - Basic Theory of Probability and Statistics (STAT 5101 and MATH 5651 are equivalencies)
- STAT 5102 - Theory of Statistics II
- STAT 5302 - Applied Regression Analysis
- STAT 5401 - Applied Multivariate Methods
- STAT 5511 - Time Series Analysis
- STAT 8051 - Advanced Regression Techniques: Linear, Nonlinear and Nonparametric methods
- STAT 8101 - Theory of Statistics I
- STAT 8102 - Theory of Statistics II
- PUBH 7401 - Fundamentals of Biostatistical Inference
- PUBH 7402 - Biostatistics Modeling and Methods
- PUBH 7440 - Introduction to Bayesian Analysis
Tier II courses
- AST/STAT 5731 - Bayesian Astrostatistics
- PUBH 7405 - Biostatistical Inference I
- PUBH 7406 - Biostatistical Inference II
- PUBH 7407 - Analysis of Categorical Data
- PUBH 7430 - Statistical Methods for Correlated Data
- PUBH 7460 - Advanced Statistical Computing
- PUBH 7485 - Methods for Causal Inference
- PUBH 8401 - Linear Models
- PUBH 8432 - Probability Models for Biostatistics
- PUBH 8442 - Bayesian Decision Theory and Data Analysis
- STAT 5052 - Statistical and Machine Learning
- STAT 5201 - Sampling Methodology in Finite Populations
- STAT 5303 - Designing Experiments
- STAT 5421 - Analysis of Categorical Data
- STAT 5601 - Nonparametric Methods
- STAT 5701 - Statistical Computing
- STAT 8112 - Mathematical Statistics II
- EE 5531 Probability and Stochastic Processes
- Any course from a list of STAT/Biostat 5xxx/8xxx classes (but not STAT 5021) with advisor and DGS approval
Take two courses (totaling 6 credits) from the following list of courses, at least one of which is a Tier I course:
Tier I courses
- CSCI 5521 - Machine Learning Fundamentals
- CSCI 5523 - Introduction to Data Mining
- CSCI 5525 - Machine Learning: Analysis and Methods
- EE 8591 - Predictive Learning from Data
- PUBH 7475 - Statistical Learning and Data Mining
- PUBH 8475/STAT 8056 - Statistical Learning and Data Mining
Tier II courses
- CSCI 5302 - Analysis of Numerical Algorithms
- CSCI 5304 - Computational Aspects of Matrix Theory
- CSCI 5511 - Artificial Intelligence I
- CSCI 5512 - Artificial Intelligence II
- CSCI 5527 - Deep Learning: Models, Computation, and Applications
- CSCI 5609 - Visualization
- CSCI 8314 - Sparse Matrix Computations
- CSCI 8581 - Big Data in Astrophysics
- EE 5239 - Intro to Nonlinear Optimization with Applications in Machine Learning and Artificial Intelligence
- EE 5251 - Optimal Filtering and Estimation
- EE 5389 - Introduction to Predictive Learning
- EE 5542 - Adaptive Digital Signal Processing
- EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence
- EE 5581 - Information Theory and Coding
- EE 5585 - Data Compression
- EE 8231 - Optimization Theory
- IE 5531 - Engineering Optimization I
- IE 8521 - Optimization
- IE 8531 - Discrete Optimization
- IE 8564 - Optimization for Machine Learning
- Any advanced class in optimization, game theory, or topic related to the listed Algorithmics courses (with advisor and DGS approval)
Take two courses (totaling 6 credits) from the following list of courses, at least one of which is a Tier I course:
Tier I courses
- CSCI 5105 - Introduction to Distributed Systems
- CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
- CSCI 5707 - Principles of Database Systems
- CSCI 5708 - Architecture and Implementation of Database Management Systems
- EE 5351 - Applied Parallel Programming
- EE 8367/CSCI 8205 - Parallel Computer Organization
Tier II courses
- CSCI 5103 - Operating Systems
- CSCI 5211 - Data Communications and Computer Networks
- CSCI 5271 - Introduction to Computer Security
- CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science
- CSCI 5801 - Software Engineering I
- CSCI 5802 - Software Engineering II
- CSCI 8102 - Foundations of Distributed Computing
- CSCI 8701 - Overview of Database Research
- CSCI 8715 - Spatial Data Science Research
- CSCI 8725 - Databases for Bioinformatics
- CSCI 8735 - Advanced Database Systems
- CSCI 8801 - Advanced Software Engineering
- EE 5355 - Algorithmic Techniques for Scalable Many-core Computing
- EE 5371 - Computer Systems Performance Measurement and Evaluation
- EE 5501 - Digital Communication
- SENG 5709 - Event Driven Architecture & Real-time Data Processing
- Any advanced class in large-scale data management or analysis, or topic related to the listed Infrastructure courses (with advisor and DGS approval)
Students must take at least one 3-credit 8xxx level course from either one of the three track requirements or an approved elective.
Other graduate-level credits to reach a total of at least 31 regular coursework credits. These can include additional courses from the three track requirements and related courses from departments in the College of Science & Engineering and the School of Statistics (but not STAT 5021). Students can use one course of independent study/directed research as an elective.
The following PUBH courses may also be used:
- PUBH 7445 - Statistics for Human Genetics and Molecular Biology
- PUBH 7461 - Exploring and Visualizing Data in R
- PUBH 8445 - Statistics for Human Genetics and Molecular Biology
- PUBH 8446 - Advanced Statistical Genetics and Genomics
- PUBH 8472 - Spatial Biostatistics
No PUBH 4xxx, 5xxx, or 6xxx level courses can be used as electives.
To use a course not specifically listed here as an elective, please send a request to the Graduate Program Coordinator ([email protected]) that includes the syllabus or detailed list of the topics covered in the course, and details on how the course relates to your capstone project. This information will be passed on to the Director of Graduate Studies for review.
Take 1 credit of the Data Science Colloquium (or equivalent in a participating department).
A colloquium course is mandatory and must appear on the student’s graduate degree plan form.
DSCI 8970 - Data Science M.S. Colloquium
OR
- CSCI 8970 - Computer Science Colloquium
See the sample graduate program outline for semester planning assistance.
Please note:
- All courses taken from a department affiliated with the Data Science program must be taken A-F if available.
- All credits listed on the Graduate Degree Plan must be 5000 level or above, with a GPA of at least 3.25. You must maintain an overall GPA of 2.8 while a graduate student in this program.
- This program may be completed with a minor.
- Use of 4xxx courses towards program requirements is not permitted
Questions?
Allison Small
Graduate Program Coordinator
Current students: [email protected]
Prospective students: [email protected]