Should we all be teaching "Intro to Data Science" instead of "Intro to Databases"?

Bill Howe, Michael J. Franklin, Juliana Freire, James Frew, Tim Kraska, Raghu Ramakrishnan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Database Community has a unique perspective on the challenges and solutions of long-term management of data and the value of data as a resource. In current computer science curricula, however, these insights are typically locked up in the context of the traditional Intro to Databases class that was developed years (or in some cases, decades) before the modern concept of Data Science arose and embedded in the discussion of legacy data management systems. We consider how to bring these concepts front and center into the emerging wave of Data Science courses, degree programs and even departments.

Original languageEnglish (US)
Title of host publicationSIGMOD 2014 - Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages917-918
Number of pages2
ISBN (Print)9781450323765
DOIs
StatePublished - 2014
Event2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014 - Snowbird, UT, United States
Duration: Jun 22 2014Jun 27 2014

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Other

Other2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014
CountryUnited States
CitySnowbird, UT
Period6/22/146/27/14

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Howe, B., Franklin, M. J., Freire, J., Frew, J., Kraska, T., & Ramakrishnan, R. (2014). Should we all be teaching "Intro to Data Science" instead of "Intro to Databases"? In SIGMOD 2014 - Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (pp. 917-918). (Proceedings of the ACM SIGMOD International Conference on Management of Data). Association for Computing Machinery. https://doi.org/10.1145/2588555.2600092