Online Program

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Thursday, May 17
Data Science
Best Practices in Data Science Education
Thu, May 17, 10:30 AM - 12:00 PM
Grand Ballroom G

Data Science for Everybody: Building and Characterizing Student-Driven Pathways in Introductory Statistics Courses (304380)

*Rebecca Nugent, Carnegie Mellon Statistics & Data Science 

Keywords: data science, introductory statistics, interactive platform, active learning

The Department of Statistics & Data Science at Carnegie Mellon is inside the Dietrich College of Humanities and Social Sciences. So while our undergraduate program teaches about a third of the campus population every semester (Statistics, Math, Computer Science, Business, etc), our introductory sequences are taken by hundreds of students with incredibly diverse future degrees ranging from English Rhetoric to Statistics & Machine Learning. With the popularity of Data Science growing as a field but largely focused on students with strong computing skills, we are in an excellent position to characterize how students with very diverse backgrounds approach or even think about Data Science. We have designed and built an interactive platform that removes the computing cognitive load and lets students explore Statistics & Data Science concepts in both structured and unstructured ways. The platform also supports student-driven inquiry and case studies. We track every click, word used, and decision made (e.g., which graphs are designed/explored before settling on a final histogram) throughout the entire data analysis pipeline from loading the data to the final written report. Models of the students' online behavior and decisions also include performance metrics as well as what areas they're choosing to study. The platform is flexible enough to allow adaptation, providing different modes of data analysis instruction, active learning opportunities, and exercises for different subsets of the population. Students are also able to build their own case studies with little restriction or faculty intervention. Data Science for everybody.