Science and data science

doi:10.1073/pnas.1702076114

. 2017 Aug 15;114(33):8689-8692.

doi: 10.1073/pnas.1702076114. Epub 2017 Aug 7.

Science and data science

David M Blei^{1

2

3}, Padhraic Smyth^{4

5}

Affiliations

¹ Department of Computer Science, Columbia University, New York, NY 10027; david.blei@columbia.edu.
² Department of Statistics, Columbia University, New York, NY 10027.
³ Data Science Institute, Columbia University, New York, NY 10027.
⁴ Department of Computer Science, University of California, Irvine, CA 92697.
⁵ Department of Statistics, University of California, Irvine, CA 92697.

PMID: 28784795
PMCID: PMC5565421
DOI: 10.1073/pnas.1702076114

Science and data science

David M Blei et al. Proc Natl Acad Sci U S A. 2017.

. 2017 Aug 15;114(33):8689-8692.

doi: 10.1073/pnas.1702076114. Epub 2017 Aug 7.

Authors

David M Blei^{1

2

3}, Padhraic Smyth^{4

5}

Affiliations

¹ Department of Computer Science, Columbia University, New York, NY 10027; david.blei@columbia.edu.
² Department of Statistics, Columbia University, New York, NY 10027.
³ Data Science Institute, Columbia University, New York, NY 10027.
⁴ Department of Computer Science, University of California, Irvine, CA 92697.
⁵ Department of Statistics, University of California, Irvine, CA 92697.

PMID: 28784795
PMCID: PMC5565421
DOI: 10.1073/pnas.1702076114

Abstract

Data science has attracted a lot of attention, promising to turn vast amounts of data into useful predictions and insights. In this article, we ask why scientists should care about data science. To answer, we discuss data science from three perspectives: statistical, computational, and human. Although each of the three is a critical component of data science, we argue that the effective combination of all three components is the essence of what data science is about.

Keywords: data science; machine learning; statistics.

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Conflict of interest statement

The authors declare no conflict of interest.

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References

1. Press G. May 28, 2013 A very short history of data science. Forbes. Available at https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of.... Accessed June 1, 2017.
1. Persons T, et al. Data and Analytics Innovation: Emerging Opportunities and Challenges. US Government Accountability Office; Washington, DC: 2016.
1. Donoho D. 2015 50 Years of Data Science, Proceedings of the Tukey Centennial Workshop (Princeton). Available at https://pdfs.semanticscholar.org/f564/25ec56586dcfd2694ab83643e9e76f314e.... Accessed June 30, 2017.
1. Ridgway J. Implications of the data revolution for statistics education. Int Stat Rev. 2015;84:528–549.
1. Dhar V. Data science and prediction. Commun ACM. 2013;56:64–73.

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[1] Press G. May 28, 2013 A very short history of data science. Forbes. Available at https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of.... Accessed June 1, 2017.

[2] Press G. May 28, 2013 A very short history of data science. Forbes. Available at https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of.... Accessed June 1, 2017.

[3] Persons T, et al. Data and Analytics Innovation: Emerging Opportunities and Challenges. US Government Accountability Office; Washington, DC: 2016.

[4] Persons T, et al. Data and Analytics Innovation: Emerging Opportunities and Challenges. US Government Accountability Office; Washington, DC: 2016.

[5] Donoho D. 2015 50 Years of Data Science, Proceedings of the Tukey Centennial Workshop (Princeton). Available at https://pdfs.semanticscholar.org/f564/25ec56586dcfd2694ab83643e9e76f314e.... Accessed June 30, 2017.

[6] Donoho D. 2015 50 Years of Data Science, Proceedings of the Tukey Centennial Workshop (Princeton). Available at https://pdfs.semanticscholar.org/f564/25ec56586dcfd2694ab83643e9e76f314e.... Accessed June 30, 2017.

[7] Ridgway J. Implications of the data revolution for statistics education. Int Stat Rev. 2015;84:528–549.

[8] Ridgway J. Implications of the data revolution for statistics education. Int Stat Rev. 2015;84:528–549.

[9] Dhar V. Data science and prediction. Commun ACM. 2013;56:64–73.

[10] Dhar V. Data science and prediction. Commun ACM. 2013;56:64–73.

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Science and data science

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Science and data science

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