TRIPODS+X:EDU: Collaborative Education: Data-driven Discovery and Alliance

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The overall goal of this project is to devise targeted exposure to data science in the context of science and engineering fields through the establishment of a multi-institutional cooperative alliance. The complexity of challenges surrounding data science necessitates collaboration, as no one organization can identify and resolve all relevant issues. The current massive demand for data scientists urgently requires training in analyzing real-world datasets and a solid understanding of the mathematics and statistics behind the methods and computational tools used in data science. Both curricula and co-curricular activities are needed to prepare undergraduates and graduate students to enter the data-driven workforce. As an initial step in this educational program, the proposed project aims to set forth coordinated initiatives that intentionally develop and administer communal data science curricula for application in the sciences and engineering. These curricula and co-curricular initiatives also broaden the participation of women and underrepresented minorities (who have been traditionally underrepresented across all STEM disciplines) in data science. Any research findings will be disseminated to scholarly venues. The proposed activity devises communal curricula units comprising two parts: concept overview and context application. The concept overview provides standardized material and contributes to data science pedagogy and practice. The context application builds upon the concept overview to bring situational awareness and domain expertise to faculty and student learners. These communal curricula units identify synergies of data science to (specific) science and engineering disciplines. Additionally, these units could be the impetus for a data science pedagogical platform available to science and engineering faculty within the alliance. Specifically, the proposed project seeks to engage in curriculum development, fill gaps in student and faculty knowledge in data science and enact a sustainability model by embedding partnerships. We will additionally develop and host one week-long data science boot camp each year for students at the participating institutions, and one half-day data science workshop each year for students and faculty. The shared technologies, tools and resources developed through this grant can be adapted for other research-intensive, women's and HBCU institutions. The overall project will provide courses and sessions to enhance domain-specific data analytical skills to both faculty and students across at least four institutions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.