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 5XX courses (12 hours) is required.

1. Core Courses for Data Science in CEE (All three courses required):

CEE 498DS

Data Science for CEE
Course description: Students will learn to leverage data to study CEE problems, identify patterns and make actionable insights. The course includes training in digital and computer tools (such as data processing, exploratory data analysis, spatial data, data visualization, distributed computing, and statistical modeling) with their applications to CEE issues.

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 Infrastructure Sensing Lab
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.
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2. Recommended Core Courses from one of the CEE technical areas 

  1. Construction Engineering and Management
  2. Construction Materials
  3. EES
  4. EWES
  5. Geotechnical Engineering
  6. Structures
  7. Transportation
  8. WRES
  9. SRIS
  10. 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:
CEE524 Construction Law
CEE525 Construction Case Studies
CEE526 Construction Optimization
CEE528 Construction Data Modeling
CEE598VSO Visual Sensing in Civil Infrastructure
CEE598BIM Building Information 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 or 445 Env Eng Principles, Biological / Air Quality Modeling 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
5XX Pick from data-driven engineering courses (Appendix B) 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
CEE509 Transportation Soils
CEE598SS Transportation Soil Stabilization

 

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 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
  • CEE 598BIM          Building Information Modeling
  • 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