Graduate Certificate in Computational Modeling
The Graduate Certificate in Computational Modeling is intended for students with little or no prior programming or computational modeling experience. The purpose of this certificate is to complement graduate students' degree programs with a set of courses that teach students critical skills in computer programming, data manipulation and visualization, and computational modeling. Students that have completed this certificate will be able to:
- Demonstrate a basic understanding of functional computer programming as applied to a range of problems in computational and data science.
- Analyze problems in terms of the algorithms and pre-existing computational tools required to solve a range of problems in computational and data science, and write a program to efficiently solve the problem.
- Construct and implement models and simulations of physical, biological, and social situations, and use these models/simulations to understand experimental or observational data.
- Apply some subset of discipline-focused or methodology-focused topics in computational and data science to solve problems in the student’s primary discipline.
The Graduate Certificate in Computational Modeling consists of at least three courses comprising a minimum of 9 credit hours, taken from the two categories listed below. The targets of the certificate program are graduate students in any discipline with interest in applying computational and data science approaches to their research problems, or who generally desire a broad education in computational modeling and computational methodology. To facilitate this goal, in addition to there being no restriction on graduate student discipline, students can apply for the certificate at any time prior to receiving their degree (either Master’s or PhD), and can apply for the certificate after taking all the necessary courses (i.e., it can be applied retroactively). The requirements are:
1. Any two of the CMSE core graduate courses (6 credits):
- CMSE-801, Introduction to Computational Modeling (3 credits)
- CMSE-802, Methods in Computational Modeling (3 credits)
- CMSE-820, Mathematical Foundations of Data Science (3 credits)
- CMSE-821, Numerical methods for differential equations
- CMSE/CSE-822, Parallel programming (3 credits)
- CMSE-823, Numerical Linear Algebra, I (3 credits)
2. One or more additional courses, which may include further CMSE courses at the 400 level or above (including from the list of core CMSE graduate courses in List 1), courses from a pre-approved list of non-CMSE courses (consult the Graduate Handbook for more information), or other computational science or data science-focused courses at the 400 level or above as approved by the CMSE graduate advisor (3 or more credits).
Note: In order to obtain this graduate certificate the student must have at least a 3.0 average in the courses that are applied to the certificate.
If you have any questions, please contact the CMSE Graduate Director.