Motivation, inclusivity, and realism should drive data science education

TL;DR

This paper emphasizes that motivation, inclusivity, and realism are essential principles for making data science education more accessible and effective across diverse communities, thereby fostering innovation.

Contribution

It introduces a teaching philosophy centered on motivation, inclusivity, and realism, with practical strategies for educators to implement these ideals in diverse educational settings.

Findings

Inclusive approaches increase participation from underrepresented groups.

Iterative curriculum updates improve engagement and learning outcomes.

Practical implementation strategies support diverse learners effectively.

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.