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Whether or when: The question on the use of theories in data science

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  • Fred Fonseca

Abstract

Data Science can be considered a technique or a science. As a technique, it is more interested in the “what” than in the “why” of data. It does not need theories that explain how things work, it just needs the results. As a science, however, working strictly from data and without theories contradicts the post‐empiricist view of science. In this view, theories come before data and data is used to corroborate or falsify theories. Nevertheless, one of the most controversial statements about Data Science is that it is a science that can work without theories. In this conceptual paper, we focus on the science aspect of Data Science. How is Data Science as a science? We propose a three‐phased view of Data Science that shows that different theories have different roles in each of the phases we consider. We focus on when theories are used in Data Science rather than the controversy of whether theories are used in Data Science or not. In the end, we will see that the statement “Data Science works without theories” is better put as “in some of its phases, Data Science works without the theories that originally motivated the creation of the data.”

Suggested Citation

  • Fred Fonseca, 2021. "Whether or when: The question on the use of theories in data science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(12), pages 1593-1604, December.
    • Handle: RePEc:bla:jinfst:v:72:y:2021:i:12:p:1593-1604
    DOI: 10.1002/asi.24537
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    References listed on IDEAS

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    1. Hamid Ekbia & Michael Mattioli & Inna Kouper & G. Arave & Ali Ghazinejad & Timothy Bowman & Venkata Ratandeep Suri & Andrew Tsou & Scott Weingart & Cassidy R. Sugimoto, 2015. "Big data, bigger dilemmas: A critical review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(8), pages 1523-1545, August.
    2. Daniel Carter & Dan Sholler, 2016. "Data science on the ground: Hype, criticism, and everyday work," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(10), pages 2309-2319, October.
    3. Robert Weinberg, 2010. "Point: Hypotheses first," Nature, Nature, vol. 464(7289), pages 678-678, April.
    4. Fred Fonseca & Michael Marcinkowski & Clodoveu Davis, 2019. "Cyber‐human systems of thought and understanding," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(4), pages 402-411, April.
    5. Michael Marcinkowski & Fred Fonseca, 2016. "The conditions of peak empiricism in big data and interaction design," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(6), pages 1279-1288, June.
    6. Todd Golub, 2010. "Counterpoint: Data first," Nature, Nature, vol. 464(7289), pages 679-679, April.
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    Cited by:

    1. Philip Fei Wu, 2023. "Veni, vidi, vici? On the rise of scrape‐and‐report scholarship in online reviews research," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(2), pages 145-149, February.

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