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. I 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.

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About Bin Yu

Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in Statistics, EECS, and Computational Biology at UC Berkeley. Her research focuses on the practice and theory of statistical machine learning, veridical data science, and solving interdisciplinary data problems in neuroscience, genomics, and precision medicine. She and her team have developed algorithms such as iterative random forests (iRF), stability-driven NMF, and adaptive wavelet distillation (AWD) from deep learning models. She was a Guggenheim Fellow in 2006, Tukey Lecturer of Bernoulli Society in 2011,  IMS Rietz Lecturer in 2016, and a COPSS E. L. Scott Awardee in 2018. At 2023 JSM, she delivered both the COPSS DAAL Lecture (formerly Fisher Award and Lecture) and the IMS Wald Lecture. She served on the inaugural scientific advisory committee of the UK Turing Institute for Data Science and AI, and is serving on the Editorial Board of PNAS. She is a member of the National Academy of Sciences and of the American Academy of Arts and Sciences, and holds an Honorary Doctorate from The University of Lausanne.