All Times ET
Keywords: Data representations, accessibility, curriculum
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 sometimes people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students of all abilities can benefit from multi-sensory data augmentation. In this proposed talk, we will 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 will share specific examples as well as open-access resources that can be utilized by other data science instructors.