List of publications in data science

Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)

:Author: Leo Breiman

:Publication data: {{Cite journal |last=Breiman |first=Leo |date=2001-08-01 |title=Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) |url=https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.full |journal=Statistical Science |volume=16 |issue=3 |doi=10.1214/ss/1009213726 |issn=0883-4237}}

:Online version: https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.pdf

:Description: Describes two cultures of statistics, one using a parsimonious and generative stochastic model, while the other is an algorithmic model with no known mechanism for how the data is generated. Breiman argues that while statistics has traditionally favored using the stochastic model, there is value in expanding the methods that statisticians can use to study phenomenon.

:Importance: Influence on the philosophies of statisticians right before the increased use of machine learning and deep learning methods. In a 20-year retrospective on this article, "Breiman's words are perhaps more relevant than ever".{{Cite journal |last=Raper |first=Simon |date=2020-01-29 |title=Leo Breiman's "Two Cultures" |url=https://academic.oup.com/jrssig/article/17/1/34/7029453 |access-date=2024-05-21 |journal=Significance |volume=17 |pages=34–37 |doi=10.1111/j.1740-9713.2020.01357.x}} Notable statisticians at the time wrote opinion pieces about the publication. Although overall critical of the publication, David Cox writes that the publication "contains enough truth and exposes enough weaknesses to be thought-provoking." Bradley Efron commented that this publication is a "stimulating paper". Emanuel Parzen also comments about this publication that "Breiman alerts us to systematic blunders (leading to wrong conclusions) that have been committed applying current statistical practice of data modeling".

Data Scientist: The Sexiest Job of the 21st Century

:Author: Thomas H. Davenport and DJ Patil

:Publication data: {{Cite news |date=2012-10-01 |title=Data Scientist: The Sexiest Job of the 21st Century |url=https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century |access-date=2025-03-27 |work=Harvard Business Review |language=en |issn=0017-8012}}

:Online version: {{URL|https://hbr.org/2022/07/is-data-scientist-still-the-sexiest-job-of-the-21st-century}}

:Description: Describes the new role at companies that is coined "Data scientist", what they do, how an organization might recruit one to their organization, and how to work with one effectively.

:Importance: This publication has been an influence on the data community as mentioned near the time it was published in 2012 by institutions like IEEE Spectrum,{{Cite web |title=Is Data Scientist the Sexiest Job of Our Time? - IEEE Spectrum |url=https://spectrum.ieee.org/is-data-scientist-the-sexiest-job-of-our-time |access-date=2025-03-27 |website=spectrum.ieee.org |language=en}} but also mentioned nearly a decade later asking the same question the title poses.{{Cite web |title=Data scientists: Still the sexiest job - if anyone would just listen to them |url=https://www.zdnet.com/article/data-scientists-still-the-sexiest-job-if-anyone-would-just-listen-to-them/ |access-date=2025-03-27 |website=ZDNET |language=en}}{{Cite web |last=Kumar |first=Krishna |date=2021-03-15 |title=Why 'Data Scientist' Will Continue To Be 'the Sexiest Job Of the 21st Century' |url=https://www.entrepreneur.com/en-in/news-and-trends/why-data-scientist-will-continue-to-be-the-sexiest/367108 |access-date=2025-03-27 |website=Entrepreneur |language=en}} In a retrospective response to their own publication 10 years earlier, authors Davenport and Patil have reflected that the role of a data scientist has "become better institutionalized, the scope of the job has been redefined, the technology it relies on has made huge strides, and the importance of non-technical expertise, such as ethics and change management, has grown".{{Cite news |date=2022-07-15 |title=Is Data Scientist Still the Sexiest Job of the 21st Century? |url=https://hbr.org/2022/07/is-data-scientist-still-the-sexiest-job-of-the-21st-century |access-date=2025-03-27 |work=Harvard Business Review |language=en |issn=0017-8012}}

50 Years of Data Science

:Author: David Donoho

:Publication data: {{Cite journal |last=Donoho |first=David |date=2017-10-02 |title=50 Years of Data Science |url=https://www.tandfonline.com/doi/full/10.1080/10618600.2017.1384734 |journal=Journal of Computational and Graphical Statistics |language=en |volume=26 |issue=4 |pages=745–766 |doi=10.1080/10618600.2017.1384734 |issn=1061-8600|doi-access=free }}

:Online version: https://www.tandfonline.com/doi/full/10.1080/10618600.2017.1384734

:Description: Retrospective discussion paper on the history and origins of data science, with a number of commentary from notable statisticians.

:Importance: This has been described as "the first in the field to present such a comprehensive and in-depth survey and overview",{{Cite journal |last=Cao |first=Longbing |date=2017-06-29 |title=Data Science: A Comprehensive Overview |url=https://dl.acm.org/doi/10.1145/3076253 |journal=ACM Computing Surveys |volume=50 |issue=3 |pages=43:1–43:42 |doi=10.1145/3076253 |issn=0360-0300|arxiv=2007.03606 }} and helps to define the field that has many definitions.

The Composable Data Management System Manifesto

:Author: Pedro Pedreira, Orri Erling, Konstantinos Karanasos, Scott Schneider, Wes McKinney, Satya R Valluri, Mohamed Zait, Jacques Nadeau

:Publication data: {{Cite journal |last1=Pedreira |first1=Pedro |last2=Erling |first2=Orri |last3=Karanasos |first3=Konstantinos |last4=Schneider |first4=Scott |last5=McKinney |first5=Wes |last6=Valluri |first6=Satya R |last7=Zait |first7=Mohamed |last8=Nadeau |first8=Jacques |date=2023-06-01 |title=The Composable Data Management System Manifesto |url=https://dl.acm.org/doi/10.14778/3603581.3603604 |journal=Proceedings of the VLDB Endowment |language=en |volume=16 |issue=10 |pages=2679–2685 |doi=10.14778/3603581.3603604 |issn=2150-8097|url-access=subscription }}

:Online version: https://www.vldb.org/pvldb/vol16/p2679-pedreira.pdf

:Description: The vision paper advocating for a paradigm shift in how data management systems are designed using standard, composable, interoperable tools rather than siloed software tools.

:Importance: A paradigm shifting view on how future data science software tools should be designed for more efficient workflows, the principles of which "will be especially crucial for addressing fragmentation, improving interoperability, and promoting user-centricity as data ecosystems grow increasingly complex".{{Cite web |last=Somrah |first=Priyanka |date=2024-04-18 |title=Distilling The Composable Data Management System Manifesto |url=https://www.work-bench.com/post/distilling-the-composable-data-management-system-manifesto |access-date=2024-05-17 |website=Work-Bench |language=en}}

Tidy Data

:Author: Hadley Wickham

:Publication data: {{Cite journal |last=Wickham |first=Hadley |date=2014-09-12 |title=Tidy Data |journal=Journal of Statistical Software |language=en |volume=59 |issue=10 |pages=1–23 |doi=10.18637/jss.v059.i10 |issn=1548-7660|doi-access=free }}

:Online version: https://www.jstatsoft.org/article/view/v059i10/ https://vita.had.co.nz/papers/tidy-data.pdf

:Description: Describes a framework for data cleaning that is summarized in the quote, "each variable is a column, each observation is a row, and each type of observational unit is a table". This allows a standard data structure for which data analysis tools can be consistently built around.

:Importance: Cited over 1,500 times, this effort for tidy data has been described by David Donoho as having "more impact on today’s practice of data analysis than many highly regarded theoretical statistics articles". In the context of data visualization, this publication is said to support "efficient exploration and prototyping because variables can be assigned different roles in the plot without modifying anything about the original dataset".{{Cite journal |last=Waskom |first=Michael |date=2021-04-06 |title=seaborn: statistical data visualization |journal=Journal of Open Source Software |volume=6 |issue=60 |pages=3021 |doi=10.21105/joss.03021 |bibcode=2021JOSS....6.3021W |issn=2475-9066|doi-access=free }}

Data Organization in Spreadsheets

:Author: Karl W. Broman and Kara H. Woo

:Publication data: {{Cite journal |last1=Broman |first1=Karl W. |last2=Woo |first2=Kara H. |date=2018-01-02 |title=Data Organization in Spreadsheets |url=https://www.tandfonline.com/doi/full/10.1080/00031305.2017.1375989 |journal=The American Statistician |language=en |volume=72 |issue=1 |pages=2–10 |doi=10.1080/00031305.2017.1375989 |issn=0003-1305}}

:Online version: https://www.tandfonline.com/doi/full/10.1080/00031305.2017.1375989

:Description: This article offers practical recommendations for organizing data in spreadsheets, like Microsoft Excel and Google Sheets, to reduce errors and lower the barrier for later analyses due to limitations in spreadsheets or quirks in the software.

:Importance: Influences teaching both data and non-data practitioners to create more analysis-friendly spreadsheets, and has been described to outline "spreadsheet best practices".{{Cite journal |last1=Estaki |first1=Mehrbod |last2=Jiang |first2=Lingjing |last3=Bokulich |first3=Nicholas A. |last4=McDonald |first4=Daniel |last5=González |first5=Antonio |last6=Kosciolek |first6=Tomasz |last7=Martino |first7=Cameron |last8=Zhu |first8=Qiyun |last9=Birmingham |first9=Amanda |last10=Vázquez-Baeza |first10=Yoshiki |last11=Dillon |first11=Matthew R. |last12=Bolyen |first12=Evan |last13=Caporaso |first13=J. Gregory |last14=Knight |first14=Rob |date=2020 |title=QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data |journal=Current Protocols in Bioinformatics |language=en |volume=70 |issue=1 |pages=e100 |doi=10.1002/cpbi.100 |issn=1934-3396 |pmc=9285460 |pmid=32343490}}

Quantitative Graphics in Statistics: A Brief History

:Author: James R. Beniger and Dorothy L. Robyn

:Publication data: {{Cite journal |last1=Beniger |first1=James R. |last2=Robyn |first2=Dorothy L. |date=1978-02-01 |title=Quantitative Graphics in Statistics: A Brief History |url=https://www.jstor.org/stable/2683467 |journal=The American Statistician |volume=32 |issue=1 |pages=1–11 |doi=10.2307/2683467 |jstor=2683467 |url-access=subscription }}

:Online version: https://www.jstor.org/stable/2683467

:Description: Outlines history and evolution of quantitative graphics in statistics, going through spatial organization (17th and 18th centuries), discrete comparison (18th and 19th centuries), continuous distribution (19th century), and multivariate distribution and correlation (late 19th and 20th centuries).

:Importance: Helps put into perspective for learning data practitioners the recency of graphics that are used. A later publication "Graphical Methods in Statistics" by Stephen Fienberg in 1979 writes that his publication "owes much to the work of Beniger and Robyn".{{Cite journal |last=Fienberg |first=Stephen E. |date=1979 |title=Graphical Methods in Statistics |url=https://www.jstor.org/stable/2683729 |journal=The American Statistician |volume=33 |issue=4 |pages=165–178 |doi=10.2307/2683729|jstor=2683729 |hdl=11299/199302 |hdl-access=free }}

Hidden Technical Debt in Machine Learning Systems

:Author: D. Sculley, Gary Holy, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison

:Publication data: {{Cite journal |last1=Sculley |first1=D. |last2=Holt |first2=Gary |last3=Golovin |first3=Daniel |last4=Davydov |first4=Eugene |last5=Phillips |first5=Todd |last6=Ebner |first6=Dietmar |last7=Chaudhary |first7=Vinay |last8=Young |first8=Michael |last9=Crespo |first9=Jean-Francois |last10=Dennison |first10=Dan |date=2015-12-07 |title=Hidden technical debt in Machine learning systems |url=https://dl.acm.org/doi/10.5555/2969442.2969519 |journal=Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 |series=NIPS'15 |location=Cambridge, MA, USA |publisher=MIT Press |pages=2503–2511 }}

:Online version: https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf

:Description: This paper argues that it is "dangerous to think of [complex machine learning] quick wins as coming for free" and overviews risk factors to account for when implementing a machine learning system.

:Importance: All authors worked for Google, article is cited over 1,000 times,Google Scholar references https://scholar.google.com/scholar?cites=2255096949091421445&as_sdt=800005&sciodt=0,15&hl=en and helped practitioners thinking about quickly implementing a machine learning tool without understanding the long-term maintenance of the tool.

A few useful things to know about machine learning

:Author: Pedro Domingos

:Publication data: {{Cite journal |last=Domingos |first=Pedro |date=2012-10-01 |title=A few useful things to know about machine learning |url=https://dl.acm.org/doi/10.1145/2347736.2347755 |journal=Communications of the ACM |volume=55 |issue=10 |pages=78–87 |doi=10.1145/2347736.2347755 |issn=0001-0782|url-access=subscription }}

:Online version: https://dl.acm.org/doi/10.1145/2347736.2347755 https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf

:Description: The purpose of this paper is to distill inaccessible "folk knowledge" to effectively implement machine learning projects because "machine learning projects take much longer than necessary or wind up producing less-than-ideal results".

:Importance: Cited over 4,000 timesGoogle Scholar references https://scholar.google.com/scholar?cites=4404716649035182981&as_sdt=40005&sciodt=0,10&hl=en&oi=gsb to influence the common set of knowledge for data practitioners using machine learning.{{Cite journal |last=Burrell |first=Jenna |date=2016-06-01 |title=How the machine 'thinks': Understanding opacity in machine learning algorithms |journal=Big Data & Society |language=en |volume=3 |issue=1 |pages=205395171562251 |doi=10.1177/2053951715622512 |issn=2053-9517|doi-access=free }}

The Introductory Statistics Course: A Ptolemaic Curriculum

:Author: George W. Cobb{{Cite web |date=2020-07-01 |title=Remembering George Cobb (1947–2020) {{!}} Amstat News |url=https://magazine.amstat.org/blog/2020/07/01/remembering-george-cobb-1947-2020/ |access-date=2024-04-21 |language=en-US}}

:Publication data: {{Cite journal |last=Cobb |first=George W |date=2007-10-12 |title=The Introductory Statistics Course: A Ptolemaic Curriculum? |url=http://dx.doi.org/10.5070/t511000028 |journal=Technology Innovations in Statistics Education |volume=1 |issue=1 |doi=10.5070/t511000028 |issn=1933-4214}}

:Online version: https://escholarship.org/uc/item/6hb3k0nz

:Description: This paper argues for a rethinking of how teachers of statistics should structure their introductory statistics courses away from the technical machinery based on the normal distribution and towards simpler alternative methods based on permutations done on computers.

:Importance: Cited over 300 times,Google Scholar references https://scholar.google.com/scholar?cites=13882980985899619210&as_sdt=800005&sciodt=0,15&hl=en&oi=gsb this paper influenced teachers of statistics in the 21st century to reconsider teaching the mere mechanics of statistics, while the use of computers can be leveraged for doing more with less.

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