Field of Study: Applied Data Science

Satellite view of networks

This problem-focused field of study:

  • Emphasizes data-driven discovery, design, and decision-making for civil and environmental engineering and health applications
  • Teaches students how to manage and analyze data using a variety of statistical, computing, and GIS programs
  • Provides students with domain-specific expertise in infrastructure and natural hazards, environmental health and technologies, water diplomacy, or other domains of interest
  • Demonstrates how theory and practice are used to solve real-world issues through a year-old colloquium series


A full-time student can complete this option (MS-non-thesis) in one year. 30 credits are required.

  1. Core (choose 2 courses – 6 credits)
    • CEE201 Applied Probability Theory (Fall)
    • CEE202 Data Analysis and Statistical Methods (Fall)
    • CEE203 Statistical Inferences and Prediction (Spring)
    • CEE204 Hypothesis Testing and Uncertainty Analysis (Spring)
  2. Data Driven Decision Making (1 credit)
    • CEE209 Problem Focused Immersion for Making Data Driven Decisions (Fall & Spring)
    • CEE292 Graduate Seminar Series - Students attend a year-long seminar series that demonstrates how data analytics are used in real-world applications. (Fall & Spring)
  3. Applied Data Science Tools, Techniques, and Methods (9 credits)
    • Machine Learning
      • CEE132 Data Science for Sustainability
      • CS135 Intro to Machine Learning and Data Mining
    • Optimization and Uncertainty Quantification
      • CEE214 Water Systems
      • CEE227 Structural Reliability
      • CEE293 Model Verification and Validation
      • UEP238 Data Science for Urban Sustainability
      • MATH190 Uncertainty Quantification
    • Spatial Analysis
      • CEE187 Geographic Information Systems
      • UEP232 Introduction to Geographic Information Systems
      • UEP236 Spatial Statistics
      • UEP239 Geospatial Programming with Python
    • Other courses can be considered with advisor approval.
  4. Domain-specific courses (12 credits) - Students obtain knowledge by taking classes in a chosen domain—infrastructure and natural hazards, environmental health and technology, water diplomacy, or in an area of the student's choosing—and learn how data are used to address key issues in these areas.

Note that full-time students may choose to complete a thesis as part of their degree program, in which case the program may take 1.5 to 2 years to complete, and thesis research (6 credits) may replace the equivalent number of course credits.


Laurie Baise: Geotechnical earthquake engineering, seismic hazard mapping, natural hazards

Shafik Islam: Water diplomacy, principled pragmatism, data driven decision making, climate and health, remote sensing, flood forecasting

Jonathan Lamontagne: Water resources, decision making under uncertainty, hydrologic statistics, integrated global change assessment

Babak Moaveni: Probabilistic system identification of structures, signal processing, bayesian inference, model updating, structural dynamics, earthquake engineering, uncertainty quantification, verification and validation of computational models

Helen Suh: Environmental health, environmental epidemiology, air pollution, exposure science, data analytics

Deborah Sunter: Science focused on energy, development and environmental management. computational modeling of electrical grid integration of renewable energy and storage, interaction of science and policy in academia, industry and government