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International Conference on Artificial Intelligence: Methodology, Systems, and Applications

AIMSA 2016: Artificial Intelligence: Methodology, Systems, and Applications pp 343–346Cite as

Artificial Intelligence in Data Science

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9883))

Abstract

Data Science programs are emerging in many areas and are related to many disciplines. This includes sciences, social sciences, business, journalism, history, and any other area dealing with massive amounts of data. People may understand that the quantity of data now available has changed the nature of research and has begun to impact the way students must prepare to be part of their discipline. However, they may not understand that artificial intelligence is a key component of the new reality. Massive amounts of data require more than computational power from computers. The size of the data collections also requires machine intelligence to organize and cluster data.

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References

  1. Anderson, P., Bowring, J., McCauley, R., Pothering, G., Starr, C.: An undergraduate degree in data science: curriculum and a decade of implementation experience. In: Proceedings of the 45th ACM Technical Symposium on Computer Science Education, SIGCSE 2016. ACM (2016)

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  3. Dichev, C., Dicheva, D., Cassel, L., Goelman, D., Posner, M.A.: Preparing all students for the data-driven world. In: Proceedings of the Symposium on Computing at Minority Institutions, ADMI 2016 (2016)

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Acknowledgments

This material is based upon work supported by the NSF Grant 1432438: IUSE Collaborative Research: Data Computing for All: Developing an Introductory Data Science Course in Flipped Format (09/01/2014-08/31/2017).

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Authors and Affiliations

  1. Department of Computing Sciences, Villanova University, Villanova, PA, USA

    Lillian Cassel, Don Goelman & Michael Posner

  2. Department of Computer Science, Winston Salem State University, Winston-Salem, NC, USA

    Darina Dicheva & Christo Dichev

Authors
  1. Lillian Cassel
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  2. Darina Dicheva
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  3. Christo Dichev
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  4. Don Goelman
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  5. Michael Posner
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Corresponding author

Correspondence to Lillian Cassel .

Editor information

Editors and Affiliations

  1. Winston-Salem State University, Winston Salem, North Carolina, USA

    Christo Dichev

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Cassel, L., Dicheva, D., Dichev, C., Goelman, D., Posner, M. (2016). Artificial Intelligence in Data Science. In: Dichev, C., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Lecture Notes in Computer Science(), vol 9883. Springer, Cham. https://doi.org/10.1007/978-3-319-44748-3_33

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  • DOI: https://doi.org/10.1007/978-3-319-44748-3_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44747-6

  • Online ISBN: 978-3-319-44748-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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