Teaching Scientific Reasoning and Data Science: UC Berkeley’s Sense & Sensibility & Science Course with Jupyter Notebook Examples

Keywords: scientific method, data science, teaching materials
Session Type: Virtual Paper Abstract
Day: Monday
Session Start / End Time: 2/28/2022 09:40 AM (Eastern Time (US & Canada)) - 2/28/2022 11:00 AM (Eastern Time (US & Canada))
Room: Virtual 47

Abstract

Most data science courses offer no protection against our human tendency to fool ourselves. This tendency is often amplified with statistics and computation, e.g. when we misread noise for signal (patternicity) or look for patterns to confirm existing expectations (confirmation bias). Scientific reasoning is a way to avoid fooling ourselves but this reasoning is often not made explicit in undergraduate education. An exception is UC Berkeley's Big Ideas course Sense & Sensibility & Science (SSS), which has been successful at teaching the philosophical underpinnings of the scientific method, how scientists make honest claims about what they know and what they do not know, and the many ways we may fool ourselves due to human psychology. SSS has been co-designed and co-taught by Nobel astrophysicist Saul Perlmutter and philosopher John Campbell and psychology faculty for the past ten years at Berkeley. While this course has traditionally focused on group decision making, in spring 2021, it also included teaching materials that integrated scientific reasoning with data science education for STEM and non-STEM majors. This presentation will highlight the conceptual framework of SSS and give an overview of the Jupyter notebooks developed in this context.