By Adam Loy (Lawrence University); Laura Chihara (Carleton College); Shonda Kuiper (Grinnell College)
This project describes a collaborative project across three institutions to develop, implement, and evaluate a series of tutorials and interdisciplinary case studies that incorporate data science and modern statistical methods related to data visualization, data wrangling, and working with date/time and text data into the undergraduate curriculum. We will outline how we successfully incorporated these materials into traditional statistics courses as well as blended courses—that is, courses in which students worked through the tutorials outside of class and then reinforced their learning through data-analytic activities in class. Discussion will also focus on how the curriculum for existing classes changed to create space for these new topics. We believe these activities provide a new pedagogical model for teaching students from all disciplines how to make data-based decisions with relevant, real-world data. These freely available tutorials have improved our institutions’ ability to address the ASA Curriculum Guidelines by providing materials that emphasize 1) facility with programing languages and database systems, 2) the ability to access and manipulate data in various ways, 3) the ability to perform algorithmic problem-solving, 4) experience working with complex data, and 5) the ability to communicate complex statistical methods in basic terms to diverse audiences as well as visualize results in an accessible manner.