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Where Are We Going with Statistical Computing? From Mathematical Statistics to Collaborative Data Science

Author

Listed:
  • Dominique Makowski

    (School of Psychology, University of Sussex, Brighton BN1 9QH, UK)

  • Philip D. Waggoner

    (Department of Data Science, YouGov & Columbia University, New York, NY 10027, USA)

Abstract

The field of statistical computing is rapidly developing and evolving. Shifting away from the formerly siloed landscape of mathematics, statistics, and computer science, recent advancements in statistical computing are largely characterized by a fusing of these worlds; namely, programming, software development, and applied statistics are merging in new and exciting ways. There are numerous drivers behind this advancement, including open movement (encompassing development, science, and access), the advent of data science as a field, and collaborative problem-solving, as well as practice-altering advances in subfields such as artificial intelligence, machine learning, and Bayesian estimation. In this paper, we trace this shift in how modern statistical computing is performed, and that which has recently emerged from it. This discussion points to a future of boundless potential for the field.

Suggested Citation

  • Dominique Makowski & Philip D. Waggoner, 2023. "Where Are We Going with Statistical Computing? From Mathematical Statistics to Collaborative Data Science," Mathematics, MDPI, vol. 11(8), pages 1-9, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1821-:d:1121200
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    References listed on IDEAS

    as
    1. Vicente-Saez, Ruben & Martinez-Fuentes, Clara, 2018. "Open Science now: A systematic literature review for an integrated definition," Journal of Business Research, Elsevier, vol. 88(C), pages 428-436.
    2. Jason S. Byers & Jeff Gill, 2022. "Applied Geospatial Bayesian Modeling in the Big Data Era: Challenges and Solutions," Mathematics, MDPI, vol. 10(21), pages 1-23, November.
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