2026 ASEE Annual Conference & Exposition

Coastal communities face unprecedented environmental challenges such as frequent and intensified floods and droughts, uncertain availability of water resources, harmful algal blooms, and deteriorating water and air quality. The management of air and water resources (AWR) becomes more complex due to ever-changing socio-environmental factors such as increasing demand caused by rapid population growth, industrial development, urbanization, and climate change. Finding convergent solutions to these issues demands well-coordinated teamwork and a new outlook toward education and research across the engineering, sciences, and humanities disciplines along with active engagement of communities. To effectively address these challenges, future environmental professionals should have an in-depth knowledge of the dynamic relationships between natural, social, and engineering systems and an ability to transcend traditional disciplinary boundaries. As part of an NSF Research Traineeship project, a transdisciplinary data science course was planned and enacted for a group of master’s and doctoral students who are interested in community research. The course was designed for graduate students in STEM majors with or without a data science background. This course focuses on providing hands-on experience and fundamental knowledge of data science processes, data analysis skills, and rapid ethnographic assessment to develop transdisciplinary solutions for environmental issues and challenges. It has two parts: data science fundamentals and qualitative informatics. Data science fundamentals encompass data and simulation-driven research and problem-solving, statistical and exploratory data analysis using R and Python, data formats, data acquisition and cleaning, cyberinfrastructure systems for data integration, data visualization, machine learning, and ethical considerations in data science. Qualitative informatics encompasses research methods central to stakeholder engagement, mixed-methodological research design, and qualitative data analysis, including maintaining fieldnotes, memoing, coding, and interpretation. In this paper, a description of the course, the need for it, and first-year outcomes are presented. The course was first offered in Fall 2024, where all students had engineering majors and a research focus in AWR. Pre- and post-participation self-reports of knowledge and skill were gathered relevant to 30 topics identified as learning objectives in the course materials. Learning and skill advancement were reported in all areas surveyed and, despite a small group size (n = 12), six of the pre- to post-participation comparisons were statistically significant. Implications of transdisciplinary challenges and the findings for the future of engineering education are discussed.