Motivation, inclusivity, and realism should drive data science education

Candace Savonen; Carrie Wright; Ava Hoffman; Elizabeth Humphries; Katherine Cox; Frederick Tan; Jeffrey Leek

doi:10.12688/f1000research.134655.2

Motivation, inclusivity, and realism should drive data science education

F1000Res. 2024 Apr 4:12:1240. doi: 10.12688/f1000research.134655.2. eCollection 2023.

Authors

Candace Savonen¹, Carrie Wright¹, Ava Hoffman¹, Elizabeth Humphries¹, Katherine Cox², Frederick Tan³, Jeffrey Leek¹

Affiliations

¹ Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA.
² Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, 21218, USA.
³ Carnegie Institute, Baltimore, Maryland, USA.

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. 20 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.

Keywords: data science; education; informatics; pedagogy; teaching.

MeSH terms

Curriculum
Data Science* / education
Humans
Motivation*
Teaching

Grants and funding

This work was supported by the National Cancer Institute under Grant UE5CA254170, the National Human Genome Research Institute under Grant U24HG010263. AMH, EH, KELC, and FJT were supported by the GDSCN through a contract to Johns Hopkins University (75N92020P00235) and the AnVIL Project through cooperative agreement awards from the National Human Genome Research Institute with cofunding from OD/ODSS to the Broad Institute (U24HG010262) and Johns Hopkins University (U24HG010263). DataTrail is supported by donations from Posit, Bloomberg Philanthropies, the Abell Foundation, and Johns Hopkins Bloomberg School of Public Health.