CMSE Undergraduate Courses
The Department of Computational Mathematics, Science and Engineering currently offers several undergraduate courses pertaining to computational and data science, as described below. We anticipate that a significant number of additional courses will be added each year - check Special Courses page or the course catalog for more information!
Please contact the CMSE Undergraduate Director or Kevin Miloshoff (miloshof@msu.edu) for additional information and updates.
Computational modeling using a wide variety of applications examples. Algorithmic thinking, dataset manipulation, model building, data visualization, and numerical methods all implemented as programs.
Prerequisite: One semester of introductory calculus.
(4 credits) Offered every fall and spring semester.
Continuation of introduction to computational modeling focusing on standard methods and tools used for modeling and data analysis. Topics may include statistical analysis, symbolic math, linear algebra, simulation techniques, data mining.
Prerequisite: CMSE 201
(4 credits) Offered every fall and spring semester.
Numerical methods in linear algebra with applications to systems of equations and eigenvalue problems, and geometry.
Prerequisite: (MTH 133 or MTH 153H or LB 119) and (CMSE 201 or CSE 231)
(3 credits) Offered every fall and spring semester.
Data science methods, including unsupervised learning and supervised learning, feature extraction, dimension reduction, clustering, regression and classification.
Prerequisites: STT 180, CMSE 201, CMSE/MTH 314, STT 380 OR (STT 441 AND STT 442)
(4 credits) Offered every fall and spring semester.
Concepts, mathematical foundations, methods, and algorithms of optimization in data modeling, all applied to modeling real-world data.
Prerequisites: CMSE 202 and CMSE 381
(4 credits) Only offered in fall semesters.
Core principles, techniques, and use of parallel computation using modern supercomputers. Parallel architectures and programming models. Message-passing and threaded programming. Principles of parallel algorithm design. Performance analysis and optimization. Prerequisites:
(CMSE 202 and CSE 232) and (MTH 126 or MTH 133 or MTH 153H or LB 119).
(4 credits) Offered in spring semester of odd-numbered years.
Core principles, methods, and techniques of effective data visualization. Visualization toolkits. Vector and scalar data. Multivariate visualization. Relationship between data analysis and visualization.
Prerequisites: CMSE 202 and (MTH 234 or MTH 254H or LB 220)
(3 credits) Offered in spring semester of even-numbered years.
Computational approaches in modern biology with a focus on applications in genomics, systems biology, evolution, and structural biology.
Prerequisite: {(CMSE 201 and LB 144 and LB 145) or (CMSE 201 and BS 161 and BS 162) or (CMSE 201 and BS 181H and BS 182H)} and (STT 200 or STT 201 or STT 231 or STT 421 or STT 351 or ECE 280)
(3 credits) Offered every spring semester.
Topics selected to supplement and enrich existing courses and lead to the development of new courses.
(1- 4 credits) Offerings and units will vary; check the course catalog.
CMSE 495 - Experiential Learning in Data Science
Team-based data science projects working with real-world data in collaboration with client/company sponsors. Practice in software development, data collection, curation, modeling, scientific visualization and presentation of results. Students may be required to sign a non-disclosure agreement (“NDA”) or an assignment of intellectual property rights (“IP Assignment”) to work with some project sponsors.
Prerequisites: CMSE 202, CMSE 382, completion of Tier I writing requirement
(4 credits) Only offered in spring semesters.
Supervised individual research or study in an area of computational or data science.
(1- 4 credits) Offered every fall and spring semester.
Contact individual CMSE faculty to arrange credit in this course.