# 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 Credits | 15.0 |

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