title: Scientific Training in the Era of Big Data: A New Pedagogy for Graduate Education.
abstract: The era of "big data" has radically altered the way scientific research is conducted and new knowledge is discovered. Indeed, the scientific method is rapidly being complemented and even replaced in some fields by data-driven approaches to knowledge discovery. This paradigm shift is sometimes referred to as the "fourth paradigm" of data-intensive and data-enabled scientific discovery. Interdisciplinary research with a hard emphasis on translational outcomes is becoming the norm in all large-scale scientific endeavors. Yet, graduate education remains largely focused on individual achievement within a single scientific domain, with little training in team-based, interdisciplinary data-oriented approaches designed to translate scientific data into new solutions to today's critical challenges. In this article, we propose a new pedagogy for graduate education: data-centered learning for the domain-data scientist. Our approach is based on four tenets: (1) Graduate training must incorporate interdisciplinary training that couples the domain sciences with data science. (2) Graduate training must prepare students for work in data-enabled research teams. (3) Graduate training must include education in teaming and leadership skills for the data scientist. (4) Graduate training must provide experiential training through academic/industry practicums and internships. We emphasize that this approach is distinct from today's graduate training, which offers training in either data science or a domain science (e.g., biology, sociology, political science, economics, and medicine), but does not integrate the two within a single curriculum designed to prepare the next generation of domain-data scientists. We are in the process of implementing the proposed pedagogy through the development of a new graduate curriculum based on the above four tenets, and we describe herein our strategy, progress, and lessons learned. While our pedagogy was developed in the context of graduate education, the general approach of data-centered learning can and should be applied to students and professionals at any stage of their education, including at the K-12, undergraduate, graduate, and professional levels. We believe that the time is right to embed data-centered learning within our educational system and, thus, generate the talent required to fully harness the potential of big data.
authors.affiliation: 1 Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.|2 Department of Computer Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.
authors.forename: Jay
authors.initials: J
authors.lastname: Aikat
authors.affiliation: 3 Odum Institute for Research in Social Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.|4 Department of Political Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Carsey
authors.affiliation: 1 Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.
authors.forename: Karamarie
authors.initials: K
authors.lastname: Fecho
authors.affiliation: 2 Department of Computer Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.
authors.forename: Kevin
authors.initials: K
authors.lastname: Jeffay
authors.affiliation: 1 Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.
authors.forename: Ashok
authors.initials: A
authors.lastname: Krishnamurthy
authors.affiliation: 5 Department of Mathematics, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Mucha
authors.affiliation: 1 Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.|6 School of Information and Library Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.
authors.forename: Arcot
authors.initials: A
authors.lastname: Rajasekar
authors.affiliation: 1 Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.|2 Department of Computer Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.
authors.forename: Stanley C
authors.initials: SC
authors.lastname: Ahalt
mesh_terms: D003479:Curriculum; D057225:Data Mining; D004500:Education, Graduate / Q000379:methods; D006801:Humans; D016247:Information Storage and Retrieval; D033183:Interdisciplinary Communication; D007857:Leadership; D013663:Teaching
keywords: big data analytics; big data infrastructure design; big data training; business intelligence; data science; graduate education; scientific discovery; team science