CyberTraining: CIU: The LSST Data Science Fellowship Program

Northwestern University, Evanston IL

Investigators

Abstract

This National Science Foundation (NSF) Training-based Workforce Development for Advanced Cyberinfrastructure award supplements graduate education in astronomy by providing in-depth training in the skills necessary to make scientific discoveries using big data. Ongoing and future surveys, such as the NSF's flagship optical telescope project, the Large Synoptic Survey Telescope (LSST), are producing data at an unprecedented rate. The sheer size of these data sets requires new working practices: sophisticated computational software and data mining procedures are necessary to fully exploit the rich information present in the data. However, these skills are not typically a core component of the astronomy and astrophysics graduate curriculum. The LSST Data Science Fellowship Program (DSFP) supplements traditional educational programs by training students in a variety of data science methods to work with and ultimately analyze big data. DSFP students are selected from a wide variety of universities using an innovative admissions procedure that increases the participation of students from underrepresented groups. Furthermore, DSFP students are trained in science communication and receive a certification in teaching data science so they can tutor peers and lead training workshops in the material learned as part of the program. The project serves the national interest, as stated by the National Science Foundation's mission: to promote the progress of science, by training the next generation of astronomers to have the computing skills necessary to derive scientific insights from the largest telescopic surveys that have ever been conducted. DSFP students attend six week-long sessions over the course of two years as part of their program training. Each session is hosted by a different institution and designed to focus on a single topic including: the basics of managing and building code, statistics, machine learning, scalable programming, data management, image processing, visualization, and science communication. This curriculum empowers trainees to ask broader questions of their data, prepares them for the technical challenges associated with LSST, and exposes them to the tools and methods necessary to advance fundamental science research. Student participants spread the adoption of data science tools, methods, and resources via the aforementioned teaching workshops, fostering new pathways to discovery in the broader research community. Students must work in collaborative groups, which in conjunction with their science communication training, enhances their leadership and mentoring skills. To reach a broad audience, all materials developed as part of the program are made available to the public, and a guide to convert the material into a semester-long course at the undergraduate or graduate level is provided. This program prepares students for success in a wide range of careers, providing education in data science methodologies, domain-specific considerations, and professional skill development in research, teaching, and communication. 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.