Graduate Certificate - Computational Modeling - Graduate Certificate

Program:
Computational Modeling - Graduate Certificate
Program Code:
8086 Computational Modeling
Program Level:
Graduate Certification
Award Type:
Graduate Certificate Program
Start Term:
US17
Department:


Excerpt from the official Academic Programs Catalog:

Listed below are the approved requirements for the program from the official Academic Programs Catalog.
Students must consult their advisors to learn which specific requirements apply to their degree programs.
Requirements as represented in Degree Navigator are not available for this program.

College of Engineering

Department of Computational Mathematics, Science and Engineering

Graduate Study
Computational Modeling - 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.