What question did this study set out to answer?

To review the 50-year evolution of data analysis from John Tukey's foundational call to modern data science initiatives, proposing a rigorous, evidence-based academic discipline for the future.

October 2, 2017Journal of Computational and Graphical StatisticsOpen Access

50 Years of Data Science

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To review the 50-year evolution of data analysis from John Tukey's foundational call to modern data science initiatives, proposing a rigorous, evidence-based academic discipline for the future.
Synthesized historical perspectives and conceptual frameworks from pioneers including John Tukey, John Chambers, Jeff Wu, Bill Cleveland, and Leo Breiman.
Evaluated contemporary university data science curricula and industry initiatives relative to classical theoretical statistics and machine learning paradigms.
Identified current data science curricula as largely commercial supersets of statistics and machine learning focused primarily on technical tools for scaling to big data.
Demonstrated that current commercial and curricular paradigms risk overlooking the scientific, evidence-based evaluation of data analysis workflows across all domains of science.

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Implication

Historical review reveals the evolution of modern data analysis in academic statistics, highlighting the need for an evidence-based science of data workflows rather than mere computational scale.

Cite This Study

David L. Donoho (2017) studied this question.

synapsesocial.com/papers/69dc60a1ac480df60a1332d7 https://doi.org/10.1080/10618600.2017.1384734

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Implication

Historical review reveals the evolution of modern data analysis in academic statistics, highlighting the need for an evidence-based science of data workflows rather than mere computational scale.

50 Years of Data Science

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Discussion

Implication

Cite This Study

50 Years of Data Science

Key Points

Discussion

Implication

Cite This Study

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