The MSDS is a 30-credit graduate degree, comprised of 18 required credits, 9 elective credits, and a 3-credit capstone course. Please visit the MSU Registrar course search page for MSU catalog course descriptions.
Six required courses (18 credits) for this program are balanced between the three units:
- STT 810, a course on probability and mathematical statistics for data scientists at MS level
- STT 811, a course on applied statistical methodology for data scientists at MS level
- CSE 482, a computer-science course on big data analysis which includes collecting, storing, preprocessing and analyzing large amounts of data.
- CSE 881, a computer-science course on data mining, at MS level.
- CMSE 830, a foundational course on algorithms and methods in Data Science at MS level
- CMSE 831, a foundational course on applied and computational optimization for data scientists, including implementation, at MS level.
9 credits of elective courses draw from a broad set of courses in the three units. Students with the 6 required courses above are well-prepared for taking electives. The list of electives includes the following, and may include other courses approved by the MS DS committee:
- STT 802, statistical computation using the specialized software R.
- STT 812, a compact course on modern statistical data analysis, including statistical learning
- STT 844, a course on time series analysis
- STT 873, a course on statistical learning and data mining
- STT 874, a course on Bayesian analysis
- STT 875, a course on R programming for statistics
- CSE 802, a course on pattern recognition
- CSE 830, a course on the design and analysis of algorithms
- CSE 840, a course on computation foundations of AI
- CSE 847, a course on machine learning
- CSE 849, a course on deep learning
- CMSE/CSE 822, a joint course on parallel computing
- CMSE 402, a course on communication in data science.
- Other CMSE elective courses which are being developed at MSU, some of which are topics
courses which have already been taught in CSME, and could be taught jointly with other
units. Plans exist for the following topics:
- CMSE 890 Uncertainty Quantification (has been taught)
- CMSE 890 Applied Topology (has been taught)
- CMSE 890 Probabilistic Graphical Models (planned)
- CMSE 890 Mathematical Image Processing (planned)
- CMSE 890 Biomedical Science Data (planned)
- CMSE 890 Applied Machine Learning for Biomedicine (planned)
- CMSE 890 Computational Methods for Machine Learning (planned)
- Other statistics topics courses STT 890 approved by the MS DS committee.
- Other computer science topics courses CSE 890 approved by the MS DS committee.
- Any graduate-level MSU course covering data science topics which can be approved by the MS DS committee.
A 3-credit capstone course involves completion of an applied, industrial, or governmental data-science project. Credit for this course can be recorded as one of the three topics courses:
- STT 890
- CSE 890
- CMSE 890
The program is building a portfolio of case studies by featuring capstone projects driven by industry, government, or academia clients.