Data Analysis Tools: Integrating Computational and Statistical Techniques in the Environmental Engineering Curriculum 

The goal of this project is to train the next generation of environmental engineers in computing and statistical techniques to solve big data problems. Current undergraduate students in the Department of Civil, Architectural and Environmental Engineering have little to no exposure to computational and statistical methods for data analysis (e.g., big data collected from sensor networks). I proposed to integrate computational techniques in several courses throughout the Environmental Engineering Degree. The plan is to integrate training in computational and statistical techniques throughout the Environmental Engineering Degree. The first activity consisted of creating tutorials in MATLAB, which are accessible to students at any time and repeated as needed. I also created templates for assignments that can be used in all courses as guidance for assignments that integrate computational and statistical techniques. The funds were also used for supporting a cohort of graduate students, called “Big Data Gurus”, that were available every week for questions from the undergraduate students. This group was beneficial particularly when students needed help outside assigned office hours. The main impact of this project will be long term and will be measurable only in terms of the research and job opportunities the students will have access to. However, a direct way of assessing impact  is to track how the material taught and the assignments are changing through time in the courses impacted by the plan. By comparing these new assignments with the old ones, I’m measuring the skills the students will be now acquiring throughout the curriculum.