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Data Science Education in Library and Information Science Schools
Author(s) -
Hagen Loni,
Andrews James,
Federer Lisa,
Benoit Gerald
Publication year - 2019
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.84
Subject(s) - curriculum , session (web analytics) , science education , best practice , information science , panel discussion , medical education , public relations , engineering ethics , computer science , psychology , mathematics education , political science , pedagogy , library science , medicine , engineering , business , world wide web , advertising , law
ABSTRACT The need for data science education has grown recently among Library and Information Science schools to better prepare information professionals for the world of big data. However, there are many challenges to providing education on data science in Library and Information Science schools. For example, developing curricula and models for managing faculty resources (full‐time teaching, buy‐out, or specialized faculty) are some initial, common challenges. Participants will present their experiences and insights regarding data science education, which may include curricula, barriers, and best practices in the panel presentations. The panel session then will open up to an active discussion session with the audience, who will be encouraged to share their experiences and insights. This panel session is part of an ongoing effort by the organizers to establish sustainable data science education in Information Science schools. Developing a framework and curricula of data science education in Information Science schools, based on best practices and informed by experience, is the optimal goal of this ongoing effort. The success of this effort depends on active participation of the participants. This panel is sponsored by SIG ED.