Kudos to Jenny Bryan and Hadley Wickham for the "Practical Data Science for Stats" PeerJ collection which is now available at:
https://peerj.com/collections/50-practicaldatascistats/The "Practical Data Science for Stats" Collection contains preprints focusing on the practical side of data science workflows and statistical analysis.
There are 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 this collection is to increase the visibility and adoption of modern data analytical workflows.
We aim to facilitate the transfer of tools and frameworks
- between industry and academia
- between software engineering and Stats/CS
- 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. We are making them available here at PeerJ to facilitate the broadest access possible. Versions of these articles are also under review for a special issue of The American Statistician, an established venue in the academic community for general-interest articles on statistical practice and teaching.
As of today, the collection includes:
Opinionated analysis development
Hilary Parker
https://doi.org/10.7287/peerj.preprints.3210v1Wrangling categorical data in R
Amelia McNamara, Nicholas J Horton
https://doi.org/10.7287/peerj.preprints.3163v2Lessons from between the white lines for isolated data scientists
Benjamin S Baumer
https://doi.org/10.7287/peerj.preprints.3160v2Teaching stats for data science
Daniel T Kaplan
https://doi.org/10.7287/peerj.preprints.3205v1Documenting and evaluating Data Science contributions in academic promotion in Departments of Statistics and Biostatistics
Lance A Waller
https://doi.org/10.7287/peerj.preprints.3204v1Modeling offensive player movement in professional basketball
Steven Wu, Luke Bornn
https://doi.org/10.7287/peerj.preprints.3201v1Excuse me, do you have a moment to talk about version control?
Jennifer Bryan
https://doi.org/10.7287/peerj.preprints.3159v2How to share data for collaboration
Shannon E Ellis, Jeffrey T Leek
https://doi.org/10.7287/peerj.preprints.3139v3The democratization of data science education
Sean Kross, Roger D Peng, Brian S Caffo, Ira Gooding, Jeffrey T Leek
https://doi.org/10.7287/peerj.preprints.3195v1Packaging data analytical work reproducibly using R (and friends)
Ben Marwick, Carl Boettiger, Lincoln Mullen
https://doi.org/10.7287/peerj.preprints.3192v1Forecasting at Scale
Sean J Taylor, Benjamin Letham
https://doi.org/10.7287/peerj.preprints.3190v1Extending R with C++: A Brief Introduction to Rcpp
Dirk Eddelbuettel, James Joseph Balamuta
https://doi.org/10.7287/peerj.preprints.3188v1How R helps Airbnb make the most of its data
Ricardo Bion, Robert Chang, Jason Goodman
https://doi.org/10.7287/peerj.preprints.3182v1Data organization in spreadsheets
Karl W Broman, Kara H. Woo
https://doi.org/10.7287/peerj.preprints.3183v1Infrastructure and tools for teaching computing throughout the statistical curriculum
Mine Cetinkaya-Rundel, Colin W Rundel
https://doi.org/10.7287/peerj.preprints.3181v1Declutter your R workflow with tidy tools
Zev Ross, Hadley Wickham, David Robinson
https://doi.org/10.7287/peerj.preprints.3180v1I look forward to reading these over the coming weeks (and figuring out ways to do journal clubs and the like).
Nick
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Nicholas Horton
Amherst College
Amherst, MA United States
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