Supporting Data Science Students from Marginalized Communities (feat. Gloria Washington)

&#8220;In designing Data Science curriculum, we should have a participatory design approach with individuals from several different communities that have been marginalized or disenfranchised by Data Science or AI techniques.&#8221;<br/><br/>In this episode, Gloria Washington, an assistant professor at Howard University, shares her experiences structuring her Data Science-driven course to accommodate students who may be hesitant to join the field; through this discussion, she also urges institutions to construct their Data Science curriculum with marginalized communities in mind. Later in the conversation, Gloria speaks on her research that utilizes human-centered data.<br/><br/>&#8220;I truly believe that there should be Data Science courses where faculty swap in and out. An applied statistic person can teach the first half of the class, a Data Scientist can teach the middle part, and a Data Ethics person can pop in-and-out at every single topic to describe how the techniques could be used against different populations/communities.&#8221; <br/><br/>Get on the email list at <a href="https://datascienceeducation.substack.com?utm_medium=podcast">datascienceeducation.substack.com</a>