Ph.D. in Scientific Computing
Examples of Approved Courses for the Ph.D. in Scientific Computing
The courses listed below are examples of what past students have taken to fulfill the program’s requirements. This is not an exhaustive list and is subject to change. Approval may also depend on your home department. Please consult with the program coordinator at [email protected] to confirm that any course you plan to take is approved, and for which group. Course plans for the Ph.D. in Scientific Computing are individualized for each student, so please confirm that any given course will work. You can email the program coordinator at [email protected] for more approved courses than are listed here.
If you are preparing a list of proposed courses for the Ph.D. in Scientific Computing audit form, please list all computational coursework you’ve taken or plan to take, whether or not it appears on this list.
If you would like to suggest a computational course for approval, please submit it for review.
Course Plan Review
A course being listed on this page does not constitute approval for your individualized course plan. Please consult with the program coordinator at [email protected] to confirm that any course you plan to take is approved, and for which group.
| AEROSP 510 | Finite Elements in Mechanical and Structural Analysis I |
| AEROSP 523 / MECHENG 523 | Computational Fluid Dynamics I |
| AEROSP 567 (formerly AEROSP 740) | Inference, Estimation & Learning |
| AEROSP 588 | Multidisciplinary Design Optimization |
| AEROSP 623 | Computational Fluid Dynamics II |
| BIOINF 527 | Introduction to Bioinformatics & Computational Biology |
| BIOSTAT 615 | Statistical Computing |
| BIOSTAT 682 | Applied Bayesian Inference |
| CEE 510 / NAVARCH 512 | Finite Element Methods In Solid And Structural Mechanics |
| CHE 554 / MATSCIE 554 | Computational Methods In MATSCIE and CHEM |
| CHEM 580 | Molecular Spectra and Structure |
| CMPLXSYS 530 | Computer Modeling of Complex Systems |
| DATASCI 500 / STATS 500 | Statistical Learning I: Regression (formerly called Applied Statistics I) |
| DATASCI 503 / STATS 503 | Statistics Learning II: Multivariate Analysis |
| EECS 545 | Machine Learning (CSE) |
| EECS 551 | Matrix Methods for Signal Processing, Data Analysis and Machine Learning |
| EECS 586 | Design and Analysis of Algorithms |
| EECS 592 | Artificial Intelligence Foundations |
| IOE 610 / MATH 660 | Linear Programming II |
| IOE 611 / MATH 663 | Nonlinear Programming |
| MATH 471 | Introduction to Numerical Methods |
| MATH 568 / BIOINF 568 | Mathematical and Computational Neuroscience |
| MATH 571 | Numerical Linear Algebra |
| MATH 572 | Numerical Methods for Scientific Computing II |
| MATH 654 | Introduction to Fluid Dynamics |
| MATH 671 | Analysis of Numerical Methods I |
| MATSCIE 554 / CHE 554 | Computational Methods In MATSCIE And CHEM |
| MECHENG 505 | Finite Element Methods In Mechanical Engineering |
| MECHENG 523 / AEROSP 523 | Computational Fluid Dynamics I |
| NAVARCH 527 / AEROSP 528/ NERS 547 | Computational Fluid Dynamics for Industrial Applications |
| NERS 547 / AEROSP 528 / NAVARCH 527 | Computational Fluid Dynamics for Industrial Applications |
| NERS 561 | Nuclear Core Design and Analysis I |
| PHYSICS 514 | Computational Physics |
| POLSCI 681 | Intermediate Game Theory |
| POLSCI 699 | Statistical Methods in Political Research |
| POLSCI 787 | Multivariate Analysis |
| PSYCH 614 | Advanced Statistical Methods |
| STATS 500 / DATASCI 500 | Statistical Learning I: Regression (formerly called Applied Statistics I) |
| STATS 503 / DATASCI 503 | Statistics Learning II: Multivariate Analysis |
Must be outside of your home department
| AEROSP 510 | Finite Elements in Mechanical and Structural Analysis I |
| AEROSP 523 / MECHENG 523 | Computational Fluid Dynamics I |
| AEROSP 567 (formerly AEROSP 740) | Inference, Estimation & Learning |
| AEROSP 623 | Computational Fluid Dynamics II |
| BIOINF 545 / BIOSTAT 646 / STATS 545 | High-throughput Molecular Genomic and Epigenomic Data Analysis |
| BIOINF 580 | Introduction to Signal Processing and Machine Learning in Biomedical Sciences |
| BIOSTAT 602 | Biostatistical Inference |
| BIOSTAT 615 | Statistical Computing |
| BIOSTAT 646 / BIOINF 545 / STATS 545 | High-throughput Molecular Genomic and Epigenomic Data Analysis |
| CMPLXSYS 530 | Computer Modeling of Complex Systems |
| DATASCI 500 / STATS 500 | Statistical Learning I: Regression (formerly called Applied Statistics I) |
| DATASCI 506 / STATS 506 | Computational Methods and Tools in Statistics |
| DATASCI 507 / STATS 507 | Data Science and Analytics using Python |
| EECS 505 | Computational Data Science and Machine Learning |
| EECS 545 | Machine Learning (CSE) |
| EECS 551 | Matrix Methods for Signal Processing, Data Analysis and Machine Learning |
| EECS 586 | Design and Analysis of Algorithms |
| EECS 587 | Parallel Computing |
| EECS 592 | Artificial Intelligence Foundations |
| MATH 568 / BIOINF 568 | Mathematical and Computational Neuroscience |
| MATH 671 | Analysis of Numerical Methods I |
| MATSCIE 556 | Molecular Simulation of Materials |
| MECHENG 523 / AEROSP 523 | Computational Fluid Dynamics I |
| MECHENG 570 | Defects in Materials and Fundamentals of Atomistic Modeling |
| NAVARCH 527 / AEROSP 528/ NERS 547 | Computational Fluid Dynamics for Industrial Applications |
| NERS 547 / AEROSP 528 / NAVARCH 527 | Computational Fluid Dynamics for Industrial Applications |
| NERS 570 / ENGR 570 | Scientific Computing |
| PHYSICS 514 | Computational Physics |
| SI 650 / EECS 549 | Information Retrieval |
| STATS 500 / DATASCI 500 | Statistical Learning I: Regression (formerly called Applied Statistics I) |
| STATS 506 / DATASCI 506 | Computational Methods and Tools in Statistics |
| STATS 507 / DATASCI 507 | Data Science and Analytics using Python |
| STATS 545 / BIOINF 545 / BIOSTAT 646 | High-throughput Molecular Genomic and Epigenomic Data Analysis |