Carnegie Mellon University
Harnessing the Data Science (R)evolution
Monday, April 10, 2017
to 5:00 PM
McMurtry Auditorium Duncan Hall
6100 Main St
Houston, Texas, USA
The use of the phrase "data science" has exploded in recent years. Companies are hiring scores of data scientists; new undergraduate and graduate programs in data science are proliferating; and students are clamoring for training in every related skill imaginable. But what is data science? And who owns it? Who is responsible for this field? No one knows the definitive answers, but no one wants to be left behind. As such, a wealth of new growth opportunities (e.g. NSF support for Foundational Data Science, Digital Humanities) co-exist with baseline-defining activities (e.g. National Academy of Science committee on Envisioning the Data Science Discipline, ASA webinars). An exciting, but overwhelming combination.
In this talk, I'll give an overview of recent Data Science Initiatives at Carnegie Mellon Statistics ranging from developing new joint programs in Statistics and Machine Learning to revamping introductory level Data Science courses for the Humanities and Social Sciences to partnering with industry for a Data Science competition series. I'll also present examples of research projects that straddle the interdisciplinary boundaries inherent in data science including characterizing civilian casualties in ongoing civil wars and building models for online foreign language acquisition. Demonstrations and illustrative examples will be included as well as wisdom gained, lessons learned, and ideas for the future.
Rebecca Nugent is the Associate Department Head, Teaching Professor, and Director of Undergraduate Studies in the Department of Statistics at Carnegie Mellon University. She received her PhD in Statistics from the University of Washington, her MS in Statistics from Stanford University, and her Bachelor's in Mathematics, Statistics, and Spanish from Rice University. Sid Rich Rules, Death from Above! Her methodological research focuses on clustering and classification in messy, high-dimensional settings. Recently she has been developing and implementing modern, interdisciplinary programmatic changes at the department and university levels. She serves on the National Academy of Science committee on Envisioning the Data Science Discipline and recently gave a TED talk on Embracing Your Inner Data Scientist.