2025 ASEE Annual Conference & Exposition

Data science is increasingly integral to various STEM domains, offering promising career opportunities across diverse engineering applications. Several studies have emphasized that the use of data science in the present engineering undergraduate curricula is mostly restricted to simple introductory subjects, usually with the use of Microsoft Excel. This restricts students' exposure to advanced data science techniques and big data applications. In Additionally, there is a lack of clear guidance on how to strengthen the synergy between common water resources and environmental engineering courses and data science.

In this paper, the authors will report on a recently introduced course entitled “Environmental Data Science,” at XXX University, incorporating the integration of Python programming for the analysis and visualization of environmental and water resources data. It is project-based, with self-directed learning opportunities. In this course, students learn how to gather and analyze data as part of the engineering design process, apply systems thinking to an engineering or societal phenomenon, collaborate with peers to find solutions, and effectively present solutions to an audience. Moreover, students are exposed to the introduction of the application of machine learning techniques to environmental datasets and Google Earth engine for remote sensing datasets.

This work will aim at reporting four main issues, namely (1) the unique components of the current integrated Data Science Course, (2) an account of selected environmental engineering projects using Python, (3) a survey result collecting data on students’ attitude and belief towards data analysis and its impact on the engineering profession, and (4) strategy to enhance synergy between data science and other engineering courses within the curriculum. It is anticipated that a thorough examination of the course's features, students' perceptions, and course synergy-enhancing factors would help to develop a guideline for data science curriculum development, implementation, and evaluation in environmental engineering.