Bridging the Gap: A Comprehensive Approach to Responsible Data Science Education

The rapid growth of AI relies on data science, but education lags behind. The book Veridical Data Science (Yu and Barter, MIT Press, 2024) addresses this gap with Predictability, Computability, and Stability (PCS) principles. It integrates these into the Data Science Life Cycle (DSLC), covering problem formulation, data cleansing and result communication. This talk explores the book's approach, compares it with traditional methods, and demonstrates PCS through examples. The talk will also describe homework types to reinforce learning and, time permitting, discuss a prostate cancer research case study to show PCS in real-world analysis.