Data Science: A New Discipline or Just Another Venn Diagram?
The emergence of Data Science has led to a flourishing of initiatives, centers, degrees, programs and organizational units at educational and research institutions around the world. The demand for data science know-how from students, parents, scientists and employers is strong and getting stronger. However, the interdisciplinary nature of the topic and the lack of a consensus around its definition raise challenges for its implementation in the modern university setting. Many ongoing efforts treat Data Science as simply a combination of topics from existing fields. While such an approach has obvious practical advantages, I believe that the challenges raised by Data Science imply that it should be more productively pursued as a new discipline in its own right. In this talk I will try to frame this larger question with a goal of initiating a discussion to identify the intellectual opportunities and research questions that could lie at the heart of a new discipline of Data Science.
Michael J. Franklin is the Liew Family Chairman of Computer Science and Sr. Advisor to the Provost for Computation and Data Science at the University of Chicago where his research focuses on database systems, data analytics, human-in-the-loop computing, and distributed computing systems. He previously was on the faculty at UC Berkeley for 17 years, where he was Chair of the CS Division and a founding Principal Investigator of the West Big Data Innovation Hub and the PI of an NSF CISE Expeditions Award focused on Big Data analytics. This latter project developed a suite of well-known open source Big Data systems including Apache Spark, SparkSQL, GraphX, MLBase and KeystoneML. Franklin is an ACM Fellow, two-time winner of the ACM SIGMOD Test of Time Award and was chosen as an “outstanding advisor” by the Berkeley CS Graduate Student Association. He currently serves as a Board Member of the Computing Research Association and on the NSF CISE Advisory Committee.