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Practical Data Science for Stats: A PeerJ Collection

1 October 2017 2,456 views 2 Comments
Nick Horton, Amherst College

    Jenny Bryan of the University of British Columbia and RStudio and Hadley Wickham of RStudio co-edited the recently published collection of papers, Practical Data Science for Stats, which are available from PeerJ.

    These preprints focus on the practical side of data science workflows and statistical analysis, particularly the many aspects of day-to-day analytical work that are almost absent from the conventional statistics literature and curriculum. And yet these activities account for a considerable share of the time and effort of data analysts and applied statisticians.

    The goal of the collection is to increase the visibility and adoption of modern data analytical workflows and facilitate the transfer of tools and frameworks between industry and academia, between software engineering and statistics/computer science, and across different domains. While these preprints have not been reviewed by PeerJ, they have been reviewed for content by the editors listed above and peers. Versions of these articles are also under review for a special issue of The American Statistician.

    A sampling of the papers in the collection include the following:

    • “Data Organization in Spreadsheets” by Karl W. Broman and Kara H. Woo
    • “Forecasting at Scale” by Sean J. Taylor and Benjamin Letham
    • “The Democratization of Data Science Education” by Sean Kross, Roger D. Peng, Brian S. Caffo, Ira Gooding, and Jeffrey T. Leek
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