Training Students Concurrently in Data Science and Team Science: Results and Lessons Learned from Multi-Institutional Interdisciplinary Student-Led Research Teams 2012-2018

• Activity Number: 256 - Contributed Poster Presentations: Section on Statistical Learning and Data Science
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Brent Ladd* and Mark Ward
• Companies: Purdue University and Purdue University
• Keywords: Data Science; R; Interdisciplinary Teams; Multi-Institutional; Diversity; Collaboration

Abstract

Dedicated training was designed and offered annually to introduce diverse cohorts of students and early-career scientists to first principles and concepts from data analysis, while also working within interdisciplinary teams. Participants completed a pre-workshop online four-week Introduction to R course. The week-long workshop emphasized hands-on tutorials with techniques for data wrangling and visualization including data scraping, parsing, cleaning, and analysis while also fostering interdisciplinary team science. Diverse backgrounds and experience were prioritized during the selection of participants, along with disciplinary interests from the full spectrum of STEM disciplines and beyond. Teams were organized around real-world, data-driven research projects. Students from statistics, math, and computer science domains were matched with students from engineering, life sciences, and liberal arts. Multi-institutional interdisciplinary teams received funds for continuing collaborative research with the goal of co-publishing results. Outcomes demonstrate that participants gain tangible data science skills and knowledge. Further, the interdisciplinary team experiences result in successful long-term student collaborations across institutions and topic domains at the nexus of data science.