Graduate Minor in Computational Engineering

The graduate minor in computational engineering gives students pursuing a graduate degree in the College of Engineering an opportunity to develop core computational and mathematical competencies to complement their coursework in engineering.

Successful completion of the minor requires that students take five courses (15.0 credits). At least three courses must come from the three of 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.

Admission to the minor requires enrollment in a College of Engineering graduate program. All College of Engineering graduate students, including BS/MS students, may pursue the minor.

Programming, Data Structures, Algorithms Requirement
Complete 1 of the following courses:3.0
Biocomputational Languages
Computer Science Foundations
Data Structures and Algorithms I
High Performance Computing
Programming Languages
Advanced Programming Techniques
Software Design
Dependable Software Systems
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
Numerical Engineering Methods
Linear Algebra & Matrix Analysis
Numerical Analysis I
Numerical Analysis II
Numerical Computing
Advanced Engineering Mathematics I
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
Decision Analysis in Public Health and Medicine
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
Managerial Statistics
Risk Assessment
Data-based Engineering Modeling
Applied Probability and Statistics I
Principles of Biostatistics
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
Data Structures and Algorithms II
Advanced Artificial Intelligence
Machine Learning
Advanced Data Structure and Algorithms
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
Statistical Inference I
Applied Multivariate Analysis
Advanced Statistical Computing
Statistical Decision Theory I
Statistical Decision Theory II
Statistical Sampling
Applied Regression Analysis
Multivariate Analysis
Total Credits15.0
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