List of publications in data science
Wikimedia list article
This is a list of publications in data science, generally organized by order of use in a data analysis workflow.
See the list of publications in statistics for more research-based and fundamental publications; while this list is more applied, business oriented, and cross-disciplinary.
General article inclusion criteria are:
Papers from notable practitioners or notable professors, either with a Wikipedia page or reference to their notability
Common knowledge all data professionals should know, with references validating this claim
Highly cited applied statistics and machine learning publications
Discussion-facilitating papers on the field of data science as a whole (for example, the Attention Is All You Need paper is arguably a landmark paper that can be added here, but it is specific to generative artificial intelligence, not for all practitioners of data)
Some reasons why a particular publication might be regarded as important:
Topic creator – A publication that created a new topic
Breakthrough – A publication that changed scientific knowledge significantly
Influence – A publication which has significantly influenced the world or has had a massive impact on the teaching of data science.
When possible, a reference is used to validate the inclusion of the publication in this list.
History
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Author: Leo Breiman
Publication data:
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". 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".
This brief starts where responsible research should: with the source description of “List of publications in data science” as wikimedia list article. Everything that follows is an evidence route, not borrowed authority.
Why this record matters
The subject matters to the general reference register because the source frames it as wikimedia list article. Its deeper value depends on whether names, dates, institutions and citations support that framing.
Vocabulary and entity names are the principal evidence signals here, because they determine the precision of every later search. The source revision retrieved here is dated Aug 31, 2026. The linked authority identifier is Q125804258. None of the 0 selected statements returned an explicit reference. The first chronological checks are 1214.
The absence of detail may reflect summary conventions rather than a lack of surviving documentation. The source lead contains qualifying language; that uncertainty should survive quotation, summary and reuse. Authority statements aid reconciliation but still require their own references, qualifiers and ranks to be checked.
How to read it
Use the entry as an orientation point, then follow its citations and revision history. Names, dates and institutional relationships should be checked against the original record.
- Subject orientation
- Search vocabulary
- Locating named sources
The closest primary source, responsible institution and strongest cited specialist reference.
Three-step research path
- Establish the record: confirm the title “List of publications in data science”, its source revision and the description used here.
- Expand the search: follow List of publications in data science primary sources, List of publications in data science archive and List research across catalogues and specialist indexes.
- Test the account: compare the strongest cited source with the responsible institution’s current record and note any disagreement.
Questions for further research
- Which source most directly establishes the central claim about “List of publications in data science”?
- What terminology or title could unlock a more precise catalogue search?
- Which cited source is closest to the event, object or claim?
Search terms from this dossier
This entry incorporates text from “List of publications in data science” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.