Abstract: “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.
Bio: Hugo Bowne-Anderson is a Data Scientist at DataCamp and has had extensive experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and with organizations such as Data Carpentry. He has developed over 25 courses on the DataCamp platform, impacting over 300,000 learners worldwide through his own courses. He previously also hosted DataFramed, the DataCamp podcast, loves teaching Bayesian data analysis and aspires to reduce as much “computational anxiety’ in the world as he can through pedagogy.
Hugo Bowne-Anderson, PhD
Data Scientist | DataCamp