Lowering Barriers to Learning and Teaching Data Science
This piece was featured in the January 2021 edition of Reflectance. View the whole issue here.
There is a widening gap between the demand for a data-literate workforce and the supply of properly trained individuals with the background in data science. This gap is even more pronounced among women, minorities, and other populations that are underrepresented in careers in Science, Technology, Engineering, and Math (STEM).
Last summer, Earth Lab’s Earth Analytics Education team completed the first year of the National Science Foundation (NSF) funded Earth Data Science Corps (EDSC), including technical workshops and a paid internship designed to help students from communities historically underserved in STEM develop critical data science skills. Participants included students and faculty from CU Boulder, Metropolitan State University of Denver, Front Range Community College, United Tribes Technical College (Bismarck, ND), and Oglala Lakota College (Kyle, SD).
Learning outcomes from the EDSC include helping students develop comfort and confidence in different technical areas that included summarizing, analyzing, and displaying data with Python, working with spatiotemporal raster and vector data, as well as the development of ‘super’ skills including communicating scientific findings and collaborating with peers in interdisciplinary teams. During their time with the EDSC, participants completed project-based internships that included researching flooding on Tribal lands, climate projections for alpine ecosystems, and COVID-19 data accesibility in Tribal communities.
Following completion of the EDSC, students demonstrated greater confidence and comfort performing technical tasks with Python, communicating their science, and collaborating with others. EDSC participants’ science identities developed as a result of completing the workshops and internships with more students identifying as scientists.
We hope to continue to help students grow in these areas in years 2 and 3 of the EDSC. This work was funded by a $1.2 million NSF Harnessing the Data Revolution (HDR) award.