Teaching Accessibly and Teaching Accessibility in Data-Intensive Courses

There is a growing need in the job market for professionals with data skills, including data literacy, data cleaning, and modeling. Institutions are offering newer data science degrees and courses and many other STEM disciplines are revising curricula to adapt to the data science needs of the 21st century. Despite the growth in the data science industry, in tech companies, some gender groups, racial groups, and people with disabilities are disproportionately underemployed. This can lead to tech products that reinforce gender and racial bias and that provide limited access to people with disabilities. 

As educators, we train the future data science workforce. Data-intensive courses can be taught accessibly to recruit students with varying backgrounds and interests to the field and data-intensive courses need to teach accessibility for the future data science workplace and products to be accessible and inclusive. In this presentation, I will share a framework for designing accessible and inclusive teaching materials for data-intensive courses designed for a diverse student body. This framework is initially designed for statistics classes but can be adopted in many data-intensive STEM fields. In addition, I will share curricular examples of teaching accessibility to students explicitly and early on in their undergraduate careers.