Appendix 4 - Graduate Course Requirements for MS Track in Data Science plus CEE
Students shall take 9 courses (36 credits) in this non-thesis Master’s track in CEE. All students must take the 3 required core courses in the Data Science track. For the remaining 6 courses, the students shall follow the recommended coursework from one of the CEE Technical Areas or Interdisciplinary Programs. A minimum of three 500-level courses (12 hours) is required.
1. Core Courses for Data Science in CEE (CEE 492, CEE 498MLC required and take either CEE 498ISL or CEE 598DL):
| CEE 492 |
Data Science for CEE |
| CEE 498MLC | Machine Learning for CEE Course description: Students will learn the fundamentals behind advanced machine learning and learn how to use machine learning tools to solve CEE problems. Topics include regression, Bayesian inference, deep neural networks, scientific deep learning, and Gaussian Processes. |
| CEE 498ISL | CE Measurement and Experiments Course description: Students will learn basic strategies for experimental design, and gain experience working with a variety of CEE sensing techniques; with components in experimental design and approaches to terrestrial, field, and laboratory-based measurements and experiential learning to explore sensor types and technologies. The course will have modules on 4 sensing applications: (1) mechanics and materials, (2) water and environment, (3) transportation, and (4) construction. |
| CEE 598DL |
Deep Learning for CEE Course description: This course focuses on deep learning within the civil and environmental engineering domain. In addition to examining the basics of deep learning, students will investigate practical applications in remote sensing, sensor data processing, information extraction, surrogate modeling, and predictive analytics. Topics of interest include deep convolutional networks, recurrent neural networks, and generative adversarial learning. Students will learn to identify, understand, and compare different deep learning techniques and formulate civil engineering problems using appropriate techniques. The focus will be on understanding why and how deep learning methods may improve civil engineering problem-solving and determining the conditions when deep learning may not be a helpful approach. Ultimately, the concepts will be leveraged to formulate and solve data-intensive real-world CEE problems using the techniques discussed. |
2. Recommended Core Courses from one of the CEE technical areas
- Construction Engineering and Management
- Construction Materials
- EES
- EWES
- Geotechnical
- Structures
- Transportation
- WRES
- SRIS
- SRHM
Recommended coursework in each CEE Technical Areas or Interdisciplinary Program
A.1. Construction Engineering and Management (CEM)
| Course number | Course name | No of credit hours |
| CEE420 | Construction Productivity | 4 |
| CEE421 | Construction Planning | 4 |
| CEE422 | Construction Cost Analysis | 4 |
| CEE5xx | Pick from CEM 500-level course list | 4 |
| CEE5xx | Pick from CEM 500-level course list | 4 |
| 5xx | Free technical elective course | 4 |
CEM 500-level course list:
| CEE521 | Building Information Modeling |
| CEE522 | Visual Data Analytics |
| CEE524 | Construction Law |
| CEE526 | Construction Optimization |
| CEE528 | Construction Data Modeling |
| CEE595 | AI in Construction Seminar |
A.2. Construction Materials (CM) Core Courses
| Course number | Course name | No of credit hours |
| CEE401 | Concrete Materials | 4 |
| CEE405 | Asphalt Materials | 4 |
| CEE504 | Infrastructure NDE Methods | 4 |
| CEE5xx | Pick from CEM 500-level course list | 4 |
| 5xx | Pick from data-driven engineering courses (Appendix B) | 4 |
| 4xx or 5xx | Free technical elective course | 4 |
CM 500-level course list:
| CEE501 | Construction Materials Characterization |
| CEE502 | Advanced Cement Chemistry |
| CEE503 | Construction Materials Deterioration |
A.3. EES Core Courses
| Course number | Course name | No of credit hours |
| CEE442 | Env Eng Principles, Physical | 4 |
| CEE443 or 447 | Env Eng Principles, Chemical / Atmos. Chemistry | 4 |
| CEE444 | Env Eng Principles, Biological | 4 |
| CEE537 | Water Quality Control Proc, I | 4 |
| CEE538 | Water Quality Control Proc, II | 4 |
| 5xx | Pick from data-driven engineering courses (Appendix B) | 4 |
A.4. EWES Core Courses
| Course number | Course name | No of credit hours |
| CEE493 | CEE 493 Sustainable Design of Engineering Technologies | 4 |
| ENG 571 | ENG 571 Theory of Energy & Sustainable Engineering | 4 |
| CEE592 | Sustainable Urban Systems | 4 |
| 5xx | Pick from data-driven engineering courses (Appendix B) | 4 |
| 4xx or 5xx | Free Technical elective course | 4 |
| 4xx or 5xx | Free Technical elective course | 4 |
A.5. Geotechnical Engineering
| Course number | Course name | No of credit hours |
| CEE483 | Soil Mechanics and Behavior | 4 |
| CEE484 | Applied Soil Mechanics | 4 |
| CEE5XX | Pick from Geotech 500-level course list | 4 |
| CEE5XX | Pick from Geotech 500-level course list | 4 |
| 4xx or 5xx | Free Technical elective course | 4 |
Geotech 500-level course list:
| CEE580 | Excavation and Support Systems |
| CEE581 | Dams, Embankments, and Slopes |
| CEE582 | Consolidation of Clays |
| CEE585 | Deep Foundations |
| CEE586 | Rock Mechanics and Behavior |
| CEE587 | Applied Rock Mechanics |
| CEE588 | Geotechnical Earthquake Engineering |
| CEE589 | Computational Geomechanics |
| CEE590 | Geotechnical field measurements |
| CEE593 | Tunneling |
A.6. Structures Core Courses
| Course number | Course name | No of credit hours |
| CEE470 | Structural Analysis | (Typically taken in undergrad) |
| CEE471 | Structural Mechanics | 4 |
| CEE462* | Steel Structures, II | Total of 8 |
| CEE463* | Reinforced Concrete II | |
| CEE472* | Structural Dynamics I | |
| CEE570 | Finite Element Methods | 4 |
| CEE5XX | Pick from Structures 500-level course list | 4 |
| 5XX | Pick from data-driven engineering courses (Appendix B) | 4 |
*take 2 out of 3 of these courses in consultation with advisor
Structures 500-level course list:
| CEE562 | Highway Bridge Design |
| CEE571 | Computational Plates and Shells |
| CEE572 | Earthquake Engineering |
| CEE573 | Structural Dynamics II |
| CEE574 | Probabilistic Loads and Design |
| CEE576 | Nonlinear Finite Elements |
| CEE577 | Computational Inelasticity |
A.7. Transportation Core Courses
| Course number | Course name | No of credit hours |
| CEE4xx or 5xx | Pick from a TE Subgroup course list | 4 |
| CEE4xx or 5xx | Pick from a TE Subgroup course list | 4 |
| CEE5XX | Pick from a TE Subgroup course list | 4 |
| CEE5xx | Pick from a TE Subgroup course list | 4 |
| 5xx | Pick from data-driven engineering courses (Appendix B) | 4 |
| 4xx or 5xx | Free technical elective | 4 |
TE Subgroup Course Lists:
Pavement and Facilities
| CEE405 | Asphalt Materials I |
| CEE406 | Pavement Design I |
| CEE415 | Geometric Design of Roads |
| CEE505 | Transportation Soil Stabilization |
| CEE506 | Pavement Design II |
| CEE508 | Pavement Evaluation & Rehabilitation |
| CEE509 | Transportation Soils |
Systems
| CEE416 | Traffic Capacity Analysis |
| CEE418 | Public Transportation Systems |
| CEE498TE | Transportation Economics |
| CEE512 | Logistic Systems Analysis |
| CEE515 | Traffic Flow Theory |
| CEE517 | Traffic Signal Systems |
| CEE598UTM | Urban Transportation Models |
Railroad
| CEE408 | Railroad Transportation Engr |
| CEE409 | Railroad Track Engineering |
| CEE410 | Railway Signaling and Control |
| CEE411 | RR Project Design & Constr |
| CEE412 | High-Speed Rail Engineering |
| CEE598RTD | Railway Terminal Design & Oper |
| CEE505 | Transportation Soil Stabilization |
| CEE509 | Transportation Soils |
A.8. WRES Core Courses
| Course number | Course name | No of credit hours |
| CEE4xx | Pick from WRES course list | 4 |
| CEE4xx | Pick from WRES course list | 4 |
| CEE4xx | Pick from WRES course list | 4 |
| CEE5xx | Pick from WRES course list | 4 |
| CEE5xx | Pick from WRES course list | 4 |
| 5xx | Pick from data-driven engineering courses (Appendix B) | 4 |
WRES Course list
| CEE434 | Environmental Systems I |
| CEE450 | Surface Hydrology |
| CEE451 | Environmental Fluid Mechanics |
| CEE457 | Groundwater |
| CEE534 | Surface Water Quality Modeling |
| CEE535 | Environmental Systems II |
| CEE550 | Hydroclimatology |
| CEE551 | Open-Channel Hydraulics |
| CEE552 | River Basin Management |
| CEE553 | River Morphodynamics |
| CEE554 | Hydrologic Variability |
| CEE555 | Mixing in Environmental Flows |
| CEE557 | Modeling of Groundwater Flow and Solute Transport |
| CEE559 | Sediment Transport |
A.9. SRIS Core Courses
| Course number | Course name | No of credit hours |
| CEE491 | Decision and Risk Analysis | 4 |
| CEE493 | Sustainable Design of Engineering Technologies | 4 |
| CEE592 | Sustainable Urban Systems | 4 |
| 5xx | Pick from data-driven engineering courses (Appendix B) | 4 |
| 5xx | Free Technical elective course | 4 |
| 4xx or 5xx | Free Technical elective course | 4 |
A.10. SRHM Core Courses
Student should take the 20-hr courses required by SRHM program plus one 500-level course from the data driven engineering course list.
List of Data-driven Engineering Courses
Data-driven courses in CEE
- CEE473 Wind Engineering
- CEE 491 Decision and Risk Analysis
- CEE498CM Computer Methods
- CEE 498LM Learning Methods for Civil Engineering
- CEE 521 Building Information Modeling
- CEE 528 Construction Data Modeling
- CEE 545 Aerosol Sampling and Analysis
- CEE 556 Hydrocomplexity
- CEE 590 Geotechnical field measurement
- CEE591 Reliability Analysis
- CEE 592 Sustainable Urban Systems
- CEE 598VSO Visual Sensing in Civil Infrastructure
- CEE598GW Globalization of Water
Data-driven courses in other departments
- CS 412 Introduction to Data Mining
- CS 424 Real-Time Systems
- CS 440 Artificial Intelligence
- CS 446 Machine Learning
- CS 450 Numerical Analysis
- CS 512 Data Mining Principles
- CS 519 Scientific Visualization
- CS 543 Computer Vision
- CS 547 Deep Learning
- CS 598 Machine Learning for Signal Processing
- ECE 410 Digital Signal Processing
- ECE 486 Control Systems
- ECE 490 Introduction to Optimization
- ECE 515 Control System Theory & Design
- ECE 534 Random Processes
- IE 410 Stochastic Processes & Application
- IE 411 Optimization of Large Systems
- IE 510 Applied Nonlinear Programming
- IE 511 Integer Programming
- GEOG 517 Geospatial Visualization & Visual Analytics
- GEOG 527 Geospatial Artificial Intelligence and Machine Learning
- GEOG 570 Advanced Spatial Analysis
- STAT 420 Methods of Applied Statistics
- STAT 431 Applied Bayesian Analysis
- STAT 448 Advanced Data Analysis
- STAT 525 Computational Statistics
- STAT 542 Statistical Learning
- MATH 564 Applied Stochastic Process (STAT 555)
- ENG 498 Interdisciplinary Methods in Research Computing