As the field of data science evolves with advancing technology and methods for working with data, so do the opportunities for re-conceptualizing how we teach undergraduate statistics and data science courses. Statistics educators need to consider how we modernize the statistics curriculum at the undergraduate level in ways that integrate multiple disciplines such as statistics, computing and data science as well as promote research-based teaching and learning practices that better support undergraduate students in developing data literacy and statistical thinking skills. In this webinar, we will focus on three crucial components: Developing research questions, professional ethics, and team collaborations that we believe will support the teaching and learning of data for majors and non-majors alike. We will share examples from two teams of undergraduate statistics or data science majors at two different stages of their development (novice and expert) while they worked on a DataFest data challenge. DataFest is a 48-hour datathon event sponsored by the American Statistical Association, where teams compete to make sense of and provide new insights into a complex, real-world data set. What we learned from studying teams’ strategies for working with data may shed light on opportunities for re-conceptualizing introductory courses to give more attention to the process of developing focused research questions when given a complex data set, professional ethics and bias, and how to collaborate effectively with others.

Jennifer Noll (TERC) 
Maria Tackett (Duke)

Teaching Statistics Section 
Contact Elinor Jones (elinor.jones@ucl.ac.uk)