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Bringing big data to the big tent

Published:25 June 2015Publication History
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Abstract

Open source tools assist data science.

References

  1. Matloff, N., The Art of R Programming: A Tour of Statistical Software Design, No Starch Press, San Francisco, CA, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Pace, L. Beginning R: An Introduction to Statistical Programming, Apress, New York, NY, 2012.Google ScholarGoogle Scholar
  3. Wickham, H. The Split-Apply-Combine Strategy for Data Analysis, Journal of Statistical Software 40 (1), April 2011.Google ScholarGoogle ScholarCross RefCross Ref
  4. Sparks, E., Talwalkar, A., Smith, V., Kottalam, J., Pan, X., Gonzalez, J., Franklin, M., Jordan, I., and Kraska, T. MLI: An API for Distributed Machine Learning, International Conference on Data Mining, Dallas, TX, Dec. 2013.Google ScholarGoogle ScholarCross RefCross Ref
  5. Curtin, R., Cline, J., Slagle, N.P., March, W., Parikshit, R., Mehta, N., and Gray, A. MLPACK: A Scalable C++ Machine Learning Library, Journal of Machine Learning Research 14, March 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Bringing big data to the big tent

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      • Published in

        cover image Communications of the ACM
        Communications of the ACM  Volume 58, Issue 7
        July 2015
        102 pages
        ISSN:0001-0782
        EISSN:1557-7317
        DOI:10.1145/2797100
        • Editor:
        • Moshe Y. Vardi
        Issue’s Table of Contents

        Copyright © 2015 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 25 June 2015

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