Data Science Across Disciplines: A Teaching Adventure in Five Acts
Sayaka Abe (Japanese Studies), David Allen (Biology), Carrie Anderson (History of Art and Architecture), Alex Lyford (Mathematics), and Caitlin Myers (Economics)
This year five faculty colleagues from Math, Art History, Biology, Economics, and Japanese designed and piloted a new winter-term course blending a traditional introduction to data science with immersive project-based applications across four disciplines. Students with no prior data science experience spent their mornings learning how to use the statistical software package R to wrangle and extract meaning from data, and their afternoons critically applying these skills to research projects on topics ranging from seventeenth-century Dutch art to tick-borne disease to Japanese pop culture to abortion policy. Join the faculty and students from this course to hear about their experiences and findings, and to discuss broader implications for providing all students equitable and inspiring access to data and digital tools.