Near-peer mentoring in data science --- two experiences at Stanford University

Abstract

Universities have been expanding the data science programs for undergraduate students. The set of new courses and research opportunities developed in this context also offer the opportunity to involve graduate students, fostering their growth as future leaders in data science education. We describe two programs that (1) provide pathways for graduate students to develop awareness and skills as teachers and mentors and (2) enhance diversity in the data science workforce. In the Data Science for Social Good Summer program, during the course of eight weeks, graduate students mentor a group of students as they tackle a data science project with social impact. They design technical training materials, tutor individual students, plan and manage projects and communication with community partner. While relying on faculty support, they effectively act as project leaders, in what it perhaps their first experience in an unsupervised mentoring and research role. The Inclusive Mentoring in Data Science course provides graduate students with training in effective and inclusive mentorship strategies. In an experiential learning framework, enrolled Stanford graduate students are paired with undergraduate students from non-R1 schools, who they mentor through a weekly one-on-one on-line meeting. The undergraduate participants are exposed to basic data science topics, projects and have the opportunity of working on professional skills. These initiatives offer a prototype of future programs that serve the dual goal of providing both hands-on mentoring experience for graduate students and research opportunities for undergraduate students, in a high-touch inclusive and encouraging environment.