The Data Science Option (DSO) equips Ph.D. students to tackle modern civil and environmental engineering challenges using large datasets, machine learning, statistical inference and visualization techniques.
The DSO is designed to meet a critical educational gap at the intersection of Civil & Environmental Engineering (CEE) and data science allowing Ph.D. students to hone modern data analysis skills that are critical for advancing research and other applications.
The DSO is intended for students with little or no background in data science, computer science or coding. The option is based on a framework developed by the University of Washington eScience Institute. The eScience Institute empowers UW researchers and students in all fields to answer fundamental questions through the use of large, complex, and noisy data, providing expertise to leverage data science tools, methods and best practices in their research and education.
CEE DSO requirements
The requirements for the CEE DSO are as follows:
- Complete courses from three out of four of the following areas:
- Software development for data science
- Statistics and machine learning
- Data management and data visualization
- Department specific requirement
- Participate in two quarters of the 1-hour eScience Community Seminar
- Fulfill all of the standard CEE Ph.D. degree requirements and UW graduate school degree requirements:
- Departmental requirements may be simultaneously fulfilled with Data Science Option requirements.
Eligibility
Full-time students in the CEE Ph.D. program who are in good standing are eligible for the DSO. At this time, the DSO is not available for the master’s degree program.
Ph.D. students may declare interest in pursuing the DSO by contacting the CEE graduate adviser. The student’s primary research adviser must approve the application.
Curriculum
A minimum of 11 credits is required (3 credits from three out of four areas and 2 credits for an eScience seminar). The 11 total credits for the DSO will count toward the minimum of 90 credits required for the CEE Ph.D. program.
Students complete courses from three out of four areas, which are detailed below. Each area lists current courses offered within CEE as well as other departments on the UW Seattle campus that satisfy the requirement.
Software development for data science
CEE options
| Course # | Course Name | Credits |
|---|---|---|
| CEE 505 | Engineering Computing | 3 |
| CEWA 599 | Geospatial Data Analysis | 4 |
Seattle campus options
| Course # | Course Name | Credits |
|---|---|---|
| CSE 583 | Software Development for Data Scientists | 4 |
| ChemE 546 | Software Engineering for Molecular Data Scientists | 3 |
| AMATH 583 | High Performance Scientific Computing | 5 |
| ME 574 | Introduction to Applied Parallel Computing for Engineers | 3 |
Statistics and machine learning
CEE options
| Course # | Course Name | Credits |
|---|---|---|
| CET 521 / IND E 546 | Inferential Data Analysis for Engineers | 3 |
| CEWA 565 | Data Analysis in Water Sciences | 4 |
| CEE 584 | Analytical Methods in Transportation I | 3 |
Seattle campus options
| Course # | Course Name | Credits |
|---|---|---|
| ATM S 552 | Objective Analysis | 3 |
| AMATH 582 | Computational Methods for Data Analysis | 5 |
| AMATH 563 | Inferring Structure of Complex Systems | 5 |
| AMATH 515 | Fundamentals of Optimization | 5 |
| CSE416 / STAT 416 | Introduction to Machine Learning | 4 |
| STAT 435 | Introduction to Statistical Machine Learning | 4 |
| CSE 546 | Machine Learning | 4 |
| STAT 535 | Statistical Learning: Modeling, Prediction, and Computing | 3 |
| STAT 509 | Introduction to Mathematical Statistics: Econometrics I | 5 |
| STAT 512-513 | Statistical Inference | 4 |
| ME/EE 578 | Convex Optimization | 3 |
| ME 599 | Machine Learning Control | 3 |
| CSE 599–special topics | Deep Reinforcement Learning | 1-5 |
| ChemE 545 | Data Science Methods for Clean Energy Research | 3 |
Data management and data visualization
CEE options
| Course # | Course Name | Credits |
|---|---|---|
| CET 522 | Transportation Data Management & Visualization | 3 |
Seattle campus options
| Course # | Course Name | Credits |
|---|---|---|
| CSE 414 | Introduction to Database Systems | 4 |
| CSE 512 | Data Visualization | 4 |
| CSE 544 | Principles of DBMS | 4 |
| HCDE 411/511 | Information for Visualization | 4 |
Department specific requirement as related to data science
CEE options
| Course # | Course Name | Credits |
|---|---|---|
| CET 513 | Transportation Networks and Optimization | 3 |
| CET 512 | Transportation Data Collection | 3 |
| CEWA 565 | Data Analysis in Water Sciences | 4 |
| CEWA 599 | Geospatial Data Analysis | 4 |
Students should contact the CEE DSO oversight committee to request review and approval for any new or existing courses not on the lists above.
eScience Community Seminar
In addition to the course requirements listed above, students must also participate in 2 quarters of the 1-credit eScience Community Seminar. This is an informal environment for presentations and discussions. Topics span science, methods, and technology across the mission of the eScience Institute. See eScience Community Seminar for additoinal details.