JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1095 10.6339/23-JDS1095 Education in Data Science Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations https://orcid.org/0000-0002-4064-6012 SeoJooYoungjseo1005@illinois.edu1∗ https://orcid.org/0000-0002-8007-934X DogucuMine23 School of Information Sciences, University of Illinois Urbana-Champaign, 614 E. Daniel St., Champaign, IL 61820, USA Department of Statistical Science, University College London, UK Department of Statistics, University of California Irvine, USA Corresponding author. Email: jseo1005@illinois.edu. 2023213202321242844131720222722023 2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2023 Open access article under the CC BY license.

Although there are various ways to represent data patterns and models, visualization has been primarily taught in many data science courses for its efficiency. Such vision-dependent output may cause critical barriers against those who are blind and visually impaired and people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students can produce data products that are more accessible. In this paper, we argue that accessibility should be taught as early as the introductory course as part of the data science curriculum so that regardless of whether learners major in data science or not, they can have foundational exposure to accessibility. As data science educators who teach accessibility as part of our lower-division courses in two different institutions, we share specific examples that can be utilized by other data science instructors.

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