  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.
