Two years ago, UC Berkeley launched a Data Science education program with a goal of bringing computational and inferential thinking in the context of real-world questions and data to the entire undergraduate community, as well as developing depth in the emerging discipline and an undergraduate major. The program has grown from zero to two thousand students in two years, starting from a freshman-level Foundations of Data Science course and growing out into a network of two dozen 'connector' and advanced courses. A key technological component of this effort is the use of the cloud reduce the barrier to entry for students, especially those not pursuing computer science related studies, and for faculty seeking to stand up a course or a data science module in an existing course. This view of the cloud as enabler extends through several aspects of the data science learning experience. The student need no more than a browser to open this domain of learning. All lectures, labs, assignments take place as a hosted Jupyter notebook. Each unfolds as a kind of computational narrative, starting from a question and relevant raw data, evolving through various visualizations and analyses to reach an observation or conclusion - a very different introductory programming experience. The infrastructure behind is sophisticated and designed to scale, but a new instructor need to do little more than populate a github repository. Other tools and services, including authentication, storage, auto-grading, assisted learning, become part of the learning environment. Advanced courses expose students to more of the technology they have been utilizing and out in the world. But, equally important is the social networks among faculty, researchers, and students that cross institutional boundaries and serve to disseminate experiences, methods and understandings.

Bio:

David Culler is the Friesen Professor of Computer Science and a member of UC Berkeley's EECS faculty since 1989. He received his B.A. from UC Berkeley in 1980, and an M.S. and Ph.D. from MIT in 1985 and 1989, respectively. He has just been appointed Interim Dean for Data Sciences, having served as Co-Director of Berkeley's Data Science Planning Initiative, Chair of the EECS Department, Faculty Director of the CITRIS Sustainable Infrastructure initiative and founding Director of Intel Research Berkeley. His research addresses the extremes of networked systems. His early work on high-performance clusters, including Berkeley Network of Workstation (NOW) Project and PlanetLab laid foundations for today's cloud. His research on embedded wireless sensor networks, including the Berkeley Motes, TinyOS, and 6LoWPAN, shaped the Internet of Things. He is currently focused on creating the robust, secure network systems infrastructure for cyberphysical systems and its data analytics, including energy efficient buildings, smart grids, and sustainable transportation. He won the Okawa Prize in 2013 and is a member of the National Academy of Engineering, an ACM Fellow, and an IEEE Fellow. He was named one of Scientific American's Top 50 Researchers and the creator of one of MIT's Technology Review's 10 Technologies that Will Change the World.