2023 ASEE Annual Conference & Exposition

In a world that is increasingly monitored, data management and analysis skills are valued and necessary in engineering. Despite the apparent advantages of a data-savvy workforce, engineering students often have negative attitudes and experiences with programming and data analysis. To improve these skill sets in engineering students, a course on environmental data analysis was developed and taught to a group of engineering graduate students (mostly civil engineering majors). The course relied on R and Excel and used R packages such as those in the tidyverse as well as modeling packages (e.g., fitdistrplus) and discipline-specific packages for accessing environmental data (e.g., DataRetrieval and cder). Real-world data sets were used in the examples and assignments: students analyzed data related to air pollution, climate, reservoir storage, water quality, and river flow. Students worked on importing data sets, data cleaning and wrangling, visualization, geospatial analyses, and modelling. Best practices integrated into the course included good and bad examples of data management, pair programming, live coding, worked examples with labeled subtasks, use of templates for assignments, and project-based learning. Student attitudes and experiences were monitored using surveys at the beginning and end of the term. Polls were conducted to assess specific teaching and learning strategies. The course structure provided a good opportunity for student collaboration and engagement. Environmental data analysis using data mining approaches and real-world data sets was a good way to engage students in programming and analysis, and to prepare them to work in an increasingly data-rich engineering workplace.