We have also become more aware of the grave consequences of treating ethics and social consequences as an afterthought, or an appendage, to data-driven services (Wing, 2018 Wing et al., 2018 Wing & Banks, 2019 Zook et al., 2017). In turn, it is clear that universities that wish to stay current must equip their students, across almost all domains, with relevant data science expertise (King, 2011 Lazowska, 2018 Moore-Sloan Data Science Environments: New York University, UC Berkeley, and the University of Washington, 2018 National Academies of Sciences, Engineering, and Medicine, 2018 Van Dusen et al., 2019 Wing et al., 2018 Wing & Banks, 2019). Keywords: data science education, transdisciplinary education, data science competency, data science literacyĭata-intensive applications increasingly impact every domain of our lives, from developing better health care outcomes, to filling out a stronger roster on our favorite sports team, to building marketing campaigns that target niche audiences. Here, we offer our experience and best practices in developing and managing the Collaboratory, which, we hope, will contribute to a blueprint for data science education leaders everywhere. Furthermore, it has cultivated a thriving ecosystem that includes a funding mechanism and a community-support structure that all contribute to its agility and success. As a result, the Collaboratory has to date served the learning needs of more than 4,000 students. Over the past 5 years, the Collaboratory has supported the development of a wide spectrum of data science pedagogical models spread across more than 40 academic departments, centers, institutes, and professional schools at Columbia University. Collaboratory educational offerings are required to be developed through a partnership between two faculty members, a data scientist and a domain expert from another field, or a larger team with complementary expertise. By offering seed funding, it fosters proactive efforts to embed data science “in context” into more traditional domains through a cohort of compelling, transdisciplinary, crowd-sourced data science education proposals each year. The novelty of the Collaboratory lies in its crowd-sourcing approach to creating new data science pedagogy and its ability to kindle transdisciplinary collaboration in doing so. This article presents the Collaboratory Program at Columbia University (termed “the Collaboratory”), which is both a set of “data science in context” educational approaches, as well as a meta-model for an accelerator program that allows different institutions to respond flexibly to their own disciplinary heterogeneity in terms of data science educational needs. Although university leadership might be aware of a growing number of successful data science acceleration programs and pedagogical models, many of which are either general purpose or specific to a particular discipline, there remains a lack of clarity about how these models might address their own specific needs. Yet, it is often unclear what resources are needed for effective data science education, and how resources ought to be prioritized. There is often broad interest in developing new data science curricula, with some universities even allocating funds toward this purpose. Many universities recognize the rapidly growing impact of data science in all fields of study and the professions and seek to embed this expertise widely across their educational offerings.
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