Graduate Minor in Computational Engineering

About the Graduate Minor

The graduate minor in Computational Engineering gives students pursuing a technical graduate degree an opportunity to develop core computational and mathematical competencies to complement their master's degree coursework.

Successful completion of the minor requires that students take five courses (15.0 credits). At least three courses must come from the three core subject areas; the student must take at least one course in each of the three core subject areas. The remaining two courses may be either core courses or elective courses.

The distinction between core and elective courses is that core courses are intended to be accessible to any College of Engineering graduate student without prerequisites. Elective courses, on the other hand, may require additional prerequisites and may be suitable only for students in certain academic disciplines or with certain academic backgrounds.

Additional Information

For more information, please contact the Department of Electrical and Computer Engineering.

Program Requirements

Programming, Data Structures, Algorithms Requirement
Complete 1 of the following courses:3.0
Advanced Biocomputational Languages
Data Structures and Algorithms
Systems Basics
Data Structures and Algorithms I
High Performance Computing
Programming Languages
Software Design
Software Reliability and Testing
Numerical Methods, Linear Algebra, Modeling and Simulation, Optimization Requirement
Complete 1 of the following courses:3.0
Biosimulation I
Transport Phenomena II
Optimization Methods for Engineering Design
Analytical and Numerical Techniques in Hydrology
Cost Benefit Analysis for Health Services
Numerical Engineering Methods
Linear Algebra & Matrix Analysis
Numerical Analysis I
Numerical Analysis II
Numerical Computing
Applied Engr Analy Methods I
Finite Element Methods I
Computational Fluid Mechanics and Heat Transfer I
Operations Research I
Advanced Mathematical Program
Operations Research Methods I
Applied Math Programming
Probability, Statistics, Machine Learning Requirement
Complete 1 of the following courses:3.0
Biomedical Statistics
Introduction to Artificial Intelligence
Special Topics in ECEC (Pattern Recognition)
Probability & Random Variables
Engineering Statistics
Risk Assessment
Data-based Engineering Modeling
Applied Probability and Statistics I
Business Statistics
Statistics for Business Analytics
Multivariate Analysis I
Statistics for Economics
Statistics for Behavioral Science
Additional Elective Courses
Complete 2 courses from the following list (or any 2 courses from the above lists):6.0
Building Energy Systems I
Intermediate Biostatistics
Interpretation of Biomedical Data
Biosimulation II
Statistical Inference I
Applied Multivariate Analysis
Advanced Statistical Computing
Data Structures and Algorithms II
Advanced Artificial Intelligence
Machine Learning
Approximation Algorithms
Computational Geometry
Cognitive Systems
Program Generation and Optimization
Parallel Programming
Parallel Programming
Random Process & Spectral Analysis
Detection & Estimation Theory
Statistical Data Analysis
Operations Research
Applied Probability and Statistics II
Applied Probability and Statistics III
Numerical Analysis III
Applied Engr Analy Methods II
Applied Engr Analy Methods III
Finite Element Methods II
Computational Fluid Mechanics and Heat Transfer II
Managerial Decision Models and Simulation
Operations Research II
System Simulation
Operations Research Methods II
Simulation Theory and Applications
Applied Regression Analysis
Multivariate Analysis
Total Credits15.0