Collaborative Research: HDR DSC: Building Capacity in Data Science through Biodiversity, Conservation, and General Education

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Solving environmental challenges relies heavily on computer science and statistical skills applied to large data sets. However, these knowledge, skills, and abilities are not evenly distributed across U.S. colleges, students, or faculty. This project works to transform environmental education by creating, implementing, and evaluating a data science training program for undergraduate students who are interested in conservation and management in urban and wild areas. Faculty from both the University of Arizona and Lewis and Clark College co-teach a foundational data science course that utilizes public data and addresses pressing environmental concerns relevant to student interests and identities. Exceptional students subsequently participate in a more focused cooperative course as data science interns for conservation stakeholders. Professional development workshops in data science education for instructional faculty, some of whom work in institutions serving primarily low-income students and students of color, prepare faculty to implement data science educational modules at their respective institutions. These critical resources and pathways allow broad communities to harness the data revolution.The workforce demand for data analysts and data scientists exceeds the current capacity for higher education to produce this skilled workforce. The overall goal of this project is development of scalable, portable data science education that can be readily incorporated into existing programs concentrating on STEM (science, technology, engineering, and mathematics), with a focus on ecology, biodiversity, and conservation. The project achieves this goal by creating multiple curricular data science on-ramps for a broad range of students early in their undergraduate training, through general education courses and foundational major courses using inclusive and expansive pedagogy techniques more common in liberal arts education. The expected outcomes from these activities are (1) development of reusable data science modules and courses that can be deployed into existing undergraduate general education and major curricula, (2) the ability for a broad range of conservation interested students to access real-world data science training they are passionate about at an early stage of their education, and (3) training and support mechanisms for undergraduate educators who wish to add data science to their curricula. The products of this proposed multi-institutional Data Science Corps program are designed to be generally extensible to other higher educational institutions and majors through open data and open science, providing capacity to rapidly deploy data science training.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.