Program Overview

Certificate of Graduate Study in Data Analytics for Water Resources

The University of Vermont's Certificate of Graduate Study in Data Analytics for Water Resources is a unique program that trains students in the use of modern data analytics tools for research and practical applications in water resources fields.

Program Overview

The aim of this Certificate of Graduate Study (CGS) program is to educate students on understanding and developing advanced methods to address critical water resources challenges, such as drinking water treatment and access, recovery and treatment of wastewater, surface and groundwater management, and adaptation to climate change and other hazards.

Concentrations

The Certificate of Graduate Study in Data Analytics for Water Resources provides training and certification for graduate students in the use of modern data analytics tools for research and practical applications in water resources fields. The core courses are focused on data analytics theory and tools, with a range of elective courses to provide opportunities for students to focus on an area that meets their individual goals.

Curriculum

The CGS in Data Analytics for Water Resources requires 12 credits (four courses). Two of those courses are the core courses that all students must take, while the other two courses can be taken from a list of electives. Students must maintain a 3.0 average in these courses to receive the CGS.

Required Core Coursework (6 credits):

• STAT 5870: Data Science I - Experience (3 credits)
• CEE 6610: Data Analytics Water Resources (3 credits)

Electives (6 credits needed):

(at least 3 credits must be from the Applications category)

Applications Category

• CEE 5550: Phys/chem Proc Water/Wastewater (3 credits)
• CEE 5560: Biological Proc Water/Wastewater (3 credits)
• CEE 5600: Principles of Hydrology (3 credits)
• CEE 5620: Advanced Hydrology (3 credits)
• CEE 5650: Groundwater Hydrology & Modeling (3 credits)
• CEE 5630: Applied River Engineering (3 credits)
• CEE 5660: Climate Change Impacts (3 credits)
• GEOL 5405: Geochemistry of Natural Waters (3 credits)
• ME 6330: Advanced Fluid Dynamics (3 credits)
• GEOL 5405: Gr Geochem of Natural Waters (3 credits)
• GEOL 5510: Geomaterial Analysis (3 credits)
• GEOL 6400: Topics in Envt & Surface Geo (1-3 credits)

Skills Category

• STAT 6300: Bayesian Statistics (3 credits)
• STAT 6870: Data Science II (3 credits)
• CEE 7900: Uncertainty & Risk Eng Systems (3 credits)
• CEE 7980: Applied Geostatistics (3 credits)
• CEE 7990: Applied Artificial Neural Networks (3 credits)
• CS 5540: Advanced Machine Learning (3 credits)
• CS 6540: Deep Learning (3 credits)
• CS 6529: Evolutionary Computation (3 credits)
• NR 5450: Data Visualization & Communication (3 credits)
• BIOL 6100: Computational Biology (4 credits)
• NR 5460: Geospatial Computation (3 credits)
• NR 5450: Data Viz and Communication (3 credits)

Admissions

The CGS in Data Analytics for Water Resources has no prerequisites except that it is open only to UVM graduate students and non-Degree post-baccalaureate students who maintain a minimum of 3.00 GPA. Students seeking to enroll in the program are required to submit a short application to the CEE Graduate Program Director.

Faculty

The program is led by experienced faculty, including Matthew J. Scarborough, Associate Professor, Department of Civil and Environmental Engineering, and Faculty Fellow, Gund Institute for Environment.

Outcomes

Students completing the CGS in Data Analytics for Water Resources program are expected to acquire a working knowledge of modern data analytics tools and their research and practical applications in water resources fields. Graduates are expected to be well recruited by industry, government labs, and doctoral programs in diverse fields. Upon completion of the program, students will have skills to:

• Develop proficiency and be conversant in standard procedures and techniques for characterization of groundwater and surface water media at environmental sites.
• Apply statistical tools to reduce dimensionality of multivariate hydrological monitoring data sets and identify key indicator parameters or driving variables.
• Apply computational tools to assess status, patterns, and trends for critical hydrological parameters and indicators over varying spatial and temporal scales.
• Synthesize and interpret hydrological data in a historical context and hydrogeologic setting to develop and visualize a conceptual site model.

Related Programs

The University of Vermont also offers related programs, including:

• Civil and Environmental Engineering (M.S.): A minimum of 30 credit hours, with a focus on civil and environmental engineering.
• Civil and Environmental Engineering (Ph.D.): A minimum of 75 credit hours, with a focus on advanced research in civil and environmental engineering.