Project Summary/Abstract Nearly all science fields have been drastically changed by the big data era, and datasets have the potential to create major breakthroughs in environmental health. However, professional development in data science lags behind for environmental health researchers and practitioners. What training does exist primarily benefits privileged institutions. We propose the Short Course in Data Science for Environmental Public Health to bridge this education gap. Through the Fred Hutchinson Cancer Center Data Science Lab, we will leverage our combined 25-plus year track record of developing educational materials, scalable courses, scalable research experiences, and building communities around data science education to create this multi-modal course. The program, which will empower 30 learners annually, begins with a two-week online course that solidifies R programming foundations. These two weeks will use a combination of didactic lectures on best practices and active hands-on lab activities to practice and engrain programming skills, a model for which the lead instructors have earned recognition for excellence in teaching and successfully used to train over 100 professional learners. Participants will practice new skills one topic at a time to make the content more manageable. This foundation will prepare participants for participating in a three-day in-person intensive “Code-a-thon” where they work on authentic environmental health projects. The Code-a-thon will allow participants to practice data ethics skills in peer code review, reproducibility, and transparency in a supportive environment. Additionally, to ensure that we are responsive to the needs of the diverse participants, we will allow learners a mechanism to provide anonymous feedback throughout and beyond the program. To create scalability, we will adapt a companion Massive Open Online Course (MOOC) so that potentially thousands of participants can benefit. We will also harness the strengths of in-person instruction by creating a yearly training for instructors hoping to reproduce this course in their own institution or community. These efforts will be bolstered by an online data community where participants can support, troubleshoot, and collaborate with peers, as well as monthly reminder newsleęers to help participants retain what they learn. We will work with our existing network of faculty from under-resourced institutions to recruit researchers and faculty specifically from Historically Black Colleges and Universities, Hispanic Serving Institutions, Tribal Colleges and Universities, and Community Colleges to participate in the live course. The course will be offered for free and half of the participants will receive travel stipends to help break down barriers to participation. Throughout, learners will work with relevant health equity datasets with the ultimate goal of understanding and addressing disparities in environmental health equity.

Project Narrative Spectacular growth in data and scalable computing represents a tremendous opportunity to create a more diverse environmental health community trained in data science principles. We propose the creation of a short course in Data Science for Environmental Public Health that combines online learning and an in-person project-focused intensive, with special consideration for participants from institutions serving underrepresented learners. Through our experience in massively scalable education, we will create a complementary online course equivalent, interactive online community, and materials aimed at instructors who want to bring this course to other institutions, empowering a broad array of potentially thousands of environmental health scientists to improve their research with data science tools.