IGE: Transforming the Education and Training of Interdisciplinary Data Scientists (TETRIDS)

点击翻译按钮获取中文摘要

Computational and data analysis skills are an increasingly important part of graduate education to prepare students for their future careers. Academic researchers collect and analyze data to advance scientific knowledge. Businesses and policy-makers use data to inform decisions. The nation needs to educate and train more students with deep technical skills in data science and broad interdisciplinary collaboration skills to work with these researchers, businesses, and policy makers to convert data into benefits for society. This National Science Foundation Innovations in Graduate Education (IGE) award to the University of Colorado Boulder will test the effectiveness of a new program to educate and train interdisciplinary data scientists who can move between theory and practice to solve problems for real-world impact. This program combines innovative classroom instruction and learning activities in the theory of interdisciplinary collaboration with practical data science experience working on real projects in the Laboratory for Interdisciplinary Statistical Analysis (LISA). Specifically, graduate students will learn how to adopt effective attitudes of collaboration, how to structure effective meetings with domain experts, what to focus on to make deep contributions to the domain, effective communication skills, and how to cultivate strong relationships with their collaborators. At the same time, students will learn how to put this knowledge into practice by collaborating with researchers, businesses, and policy makers to apply data science to solve a wide variety of domain-specific problems for decision-making. Ultimately, this program may lead to a transformation in who can be trained to become data scientists, where they can be trained, and what can be achieved when we transform data into societal benefits. The goal of this IGE project is to evaluate how effectively the LISA program educates and trains graduate students from a variety of backgrounds to become collaborative data scientists. Specifically, this project will combine assessments of students’ technical skills, student self-evaluation surveys, LISA administrative records, domain expert feedback surveys, and independent expert evaluations of students’ projects to answer two research questions: 1. To what extent are LISA students effective interdisciplinary data science collaborators? (i.e., how well do they do on their projects compared to historical norms, other cohorts of students, and an expert collaborative data scientist?) 2. What degree of technical preparation in data science is sufficient for graduate students to become effective interdisciplinary data science collaborators? Knowledge generated from this project may inform best practices across colleges and universities for thousands of graduate students to be educated and trained to become effective interdisciplinary data scientists. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community.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.