This J-term, midd.data mounted our fourth iteration of Data Science Across Disciplines, and it was another big success! Six instructors—Alex Lyford (Mathematics and Statistics), Bert Johnson (Political Science), Pete Nelson (Geography), Katherine O’Brien (Center for Community Engagement), Conor Stinson (‘06.5) and Michael Czekanski (‘20)—worked with fifty students spanning fourteen different declared majors to solve a range of challenging, data-driven problems, while learning the necessary data science and computational tools along the way!
On October 9, Yale political scientist Joshua Kalla visited Middlebury to give a talk on “Polarization and persuasion in American Politics,” co-sponsored by midd.data, the Conflict Transformation Collaborative, and the Rohatyn Center for Global Affairs. Professor Kalla discussed evidence from field experiments demonstrating that under certain conditions, perspective taking and storytelling may shift exclusionary attitudes and policy preferences.
This summer midd.data offered our credit-bearing “Introduction to Data” summer course for a second time, following our successful launch last year. This course is designed to provide students from backgrounds that have historically been underrepresented in data science with new opportunities to learn about and participate in the field.
In the summer of 2022, midd.data launched a new credit-bearing “Introduction to Data” summer course designed to provide students from backgrounds that have historically been underrepresented in data science with new opportunities to learn about and participate in the field.
Jeff Sawyer from The Center for Careers and Internships (CCI) shares information about career opportunities in the technology and data, and how to work with CCI to explore.
Dr. Hang Du of Middlebury’s Chinese Department has for over a decade been building and analyzing a database of spoken Chinese to understand how student’s studying abroad learn Chinese. We checked in with her to see how she is doing this work, what she has learned, and what advice she has for others embarking on this type of study.
This winter term five faculty colleagues from Math, Art History, Biology, Economics, and Japanese designed and piloted a new 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.
