Friday, March 17, 8:00 AM MST
How Can We Prepare Today's Students for their Data-empowered Futures? What should future statisticians, CEOs, and senators know about the history and ethics of data.
Automated and algorithmic decision systems will increasingly shape their realities, as citizens and employees in quantified workplaces and data-conversant environments. This talk will introduce lessons learned in the creation of a class at Columbia which takes a historical approach to engaging with data, both critically (in readings and discussion) and functionally (in computational "labs”, written in Python and which pair with the readings). The class is taught without prerequisites, aimed at training future statisticians in the context of data as well as future product developers and policy makers as to how best to understand data and data practitioners.
Materials from the course can be found here; the syllabus closely mirrors the forthcoming book "How Data Happened: A History from the Age of Reason to the Age of Algorithms" to be published by Norton Press in March 2023.
Chris Wiggins is an associate professor of applied mathematics at Columbia University and the Chief Data Scientist at The New York Times. At Columbia he is a founding member of the executive committee of the Data Science Institute, and of the Department of Systems Biology, and is affiliated faculty in Statistics. He is a co-founder and co-organizer of hackNY, a nonprofit which since 2010 has organized once a semester student hackathons, and the hackNY Fellows Program, a structured summer internship at NYC startups. Prior to joining the faculty at Columbia he was a Courant Instructor at NYU (1998–2001) and earned his PhD at Princeton University (1993–1998) in theoretical physics.
He is a Fellow of the American Physical Society and is a recipient of Columbia’s Avanessians Diversity Award.