We shall discuss the various ways in which data science is approached by different communities, including Statistics, Machine-Learning, and Database communities. Each presents a different viewpoint and values different outcomes. Some consequences of these approaches will be discussed. We also contrast approaches to education of the large number of data scientists that are expected to be required in the near future.
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Bio: Jeff Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp. He received the B.S. degree from Columbia University in 1963 and the PhD from Princeton in 1966.
Before his appointment at Stanford in 1979, Jeff was a member of the technical staff of Bell Laboratories from 1966–69, and on the faculty of Princeton University between 1969 and 1979. From 1990–94, he was chair of the Stanford Computer Science Department.
Jeff was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom Outstanding Educator Award (1998), the Knuth Prize (2000), the Sigmod E. F. Codd Innovations award (2006), and the IEEE von Neumann medal (2010). He is the author of 16 books, including books on database systems, compilers, automata theory, and algorithms.