“Data scientist” has been called the sexiest job of the 21st Century but it’s well on its way to becoming the most dangerous: the state of the art is a collection of siloed, ad hoc techniques developed to solve important and interesting challenges without a robust, principled approach. To paraphrase Michael I. Jordan, we are building dangerous, planetary-scale inference-and-decision making systems without a sufficiently stable engineering discipline, just as people built bridges and buildings, many of which fell, before there was civil engineering. In this talk, we’ll delve into how we got where we are today, what types of large-scale systems we’re building in medicine, information technology, finance, transport and society at large, and paths forward.
Dr. Hugo Bowne-Anderson is a data scientist and educator at DataCamp and host of the podcast DataFramed. He has worked in applied math research in cell biology at Yale University and the Max Planck Institute for Cell Biology and Genetics, after receiving his PhD in Pure Mathematics at the University of New South Wales. He joined DataCamp four years ago to build out their foundational data science curriculum in Python and his main interests now are promoting data and AI literacy, helping to spread data skills through organizations.