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Department of Civil and Environmental Engineering

Field of Study: Applied Data Science

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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

Requirements:

A full-time student can complete this option (MS-non-thesis) in one year. Ten courses are required: 2 core classes, 4 modular tool-based training elements (6 SHU), 4 classes in a research domain, a seminar (3 SHUs), and a capstone project (3 SHUs).

  1. Core classes (2 courses – 6 SHUs) - In the core classes, students learn data analysis theory and methods.
  2. Analytical toolkit (6 SHUs) - The toolkit trains students in key statistical and computer programs, GIS methods, and data processing and management.
  3. Domain-specific classes (4 courses – 12 SHUs) - 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.
  4. Design experience/internship (3 SHUs) - Students obtain hands-on experience applying data analytics and tools to address a question or topic of their choosing.
  5. Seminar series (3 SHUs) - Students attend a year-long seminar series that demonstrates how data analytics are used in real-world applications.

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 SHUs) may replace the equivalent number of course credits in domain-specific courses.