Perspectives on Inclusive Teaching in Introductory Data Science: Best Practices for University and Community College Students

Data science is a growing and evolving field, and as such, lacks standard references and resources on methods of teaching. To meet this need, more discussion around best practices for training data science students of diverse backgrounds is a necessary step to ensuring quality learning experiences. As Data Science Preceptors (PostDoctoral Teaching Staff), we have experience teaching introductory data science courses at the University of Chicago and City Colleges of Chicago, as well as during a summer intensive program. Through these experiences, we learned the importance of using varied pedagogical approaches that match student needs. During this session, we will discuss these teaching methods including lecturing, project-based learning, and classroom activities and how they affect student learning. We will focus on similarities and differences in student learning in small and large classroom settings, universities and community colleges, and traditional classes and summer programs. Our hope is that this session will lead to a fruitful discussion that provides insight on how to teach an introductory data science course that is adaptable to institution type, class size, and student background.