Motivation, inclusivity, and realism should drive data science education
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Published:2023-09-28
Issue:
Volume:12
Page:1240
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ISSN:2046-1402
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Container-title:F1000Research
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language:en
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Short-container-title:F1000Res
Author:
Savonen CandaceORCID,
Wright Carrie,
Hoffman Ava,
Humphries Elizabeth,
Cox Katherine,
Tan Frederick,
Leek Jeffrey
Abstract
Data science education provides tremendous opportunities but remains inaccessible to many communities. Increasing the accessibility of data science to these communities not only benefits the individuals entering data science, but also increases the field's innovation and potential impact as a whole. Education is the most scalable solution to meet these needs, but many data science educators lack formal training in education. Our group has led education efforts for a variety of audiences: from professional scientists to high school students to lay audiences. These experiences have helped form our teaching philosophy which we have summarized into three main ideals: 1) motivation, 2) inclusivity, and 3) realism. To put these ideals better into practice, we also aim to iteratively update our teaching approaches and curriculum as we find ways to better reach these ideals. In this manuscript we discuss these ideals as well practical ideas for how to implement these philosophies in the classroom.
Funder
National Human Genome Research Institute
Johns Hopkins University
National Cancer Institute
Publisher
F1000 Research Ltd
Subject
General Pharmacology, Toxicology and Pharmaceutics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine
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