Graduate Certificate

The Graduate Certificate in Computational Modeling is intended for students with interest in applying computational and data science approaches to their research problems, or who generally desire broad training in computational modeling and methodology.

Requirements for the Graduate Certificate in Computational Modeling

Students must complete a minimum of 9 credits from the following:

1.	Two of the following core courses (6 credits):
	CMSE	801	Introduction to Computational Modeling						3
	CMSE	802	Methods in Computational Modeling						3
	CMSE	820	Mathematical Foundations of Data Science						3
	CMSE	821	Numerical Methods for Differential Equations						3
	CMSE	822	Parallel Computing						3
	CMSE	823	Numerical Linear Algebra I						3
2.	One or more additional courses selected from the following:
	AST	911	Numerical Techniques in Astronomy						2
	CEM	883	Computational Quantum Chemistry						3
	CEM	888	Computational Chemistry						3
	CMSE	801	Introduction to Computational Modeling						3
	CMSE	802	Methods in Computational Modeling						3
	CMSE	820	Mathematical Foundations of Data Science						3
	CMSE	821	Numerical Methods for Differential Equations						3
	CMSE	822	Parallel Computing						3
	CMSE	823	Numerical Linear Algebra I						3
	CSE	836	Probabilistic Models and Algorithms in Computational Biology						3
	CSE	845	Multi-disciplinary Research Methods for the Study of Evolution						3
	CSE	881	Data Mining						3
	ECE	837	Computational Methods in Electromagnetics						3
	ME	835	Turbulence Modeling and Simulation						3
	ME	840	Computational Fluid Dynamics and Heat Transfer						3
	ME	872	Finite Element Method						3
	MTH	451	Numerical Analysis I						3
	MTH	452	Numerical Analysis II						3
	MTH	850	Numerical Analysis I						3
	MTH	851	Numerical Analysis II						3
	MTH	852	Numerical Methods for Ordinary Differential Equations						3
	MTH	950	Numerical Methods for Partial Differential Equations I						3
	MTH	951	Numerical Methods for Partial Differential Equations II						3
	MTH	995	Special Topics in Numerical Analysis and Operations Research						3 to 6
	PHY	480	Computational Physics						3
	PHY	915	Computational Condensed Matter Physics						2
	PHY	919	Modern Electronic Structure Theory						2
	PHY	950	Data Analysis Methods for High-Energy and Nuclear Physics						2
	PHY	998	High Performance Computing and Computational Tools for Nuclear Physics						2
	PLB	810	Theories and Practices in Bioinformatics						3
	QB	826	Introduction to Quantitative Biology Techniques						1
	STT	461	Computations in Probability and Statistics						3
	STT	465	Bayesian Statistical Methods						3
	STT	802	Statistical Computation						3
	STT	874	Introduction to Bayesian Analysis						3
	Courses used to fulfill requirement 1. may not be used to fulfill this requirement. Additional courses at the 400-level or above may be used to fulfill this requirement if approved by the CMSE graduate advisor. Students must have a minimum 3.0 grade-point average in courses applied to the certificate in order for it to be awarded.