Challenges and Opportunities for Statistics in the Era ofData Science
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10506798" target="_blank" >RIV/00216208:11320/25:10506798 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=K.g0C-_RE2" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=K.g0C-_RE2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1162/99608f92.abf14c9d" target="_blank" >10.1162/99608f92.abf14c9d</a>
Alternative languages
Result language
angličtina
Original language name
Challenges and Opportunities for Statistics in the Era ofData Science
Original language description
Statistics as a scientific discipline is currently facing the great challenge of finding its place in data science once more. At the beginning of the last century, the development of the discipline of statistics was initiated by data-related research questions. Nowadays, it is often viewed to have not kept up with the current developments in data science, which are largely focused on algorithmic, exploratory, and computational aspects and often driven by other disciplines, such as computer science. However, statistics can-and should-contribute to the advances of data science. Of most interest are the strengths of statistics, such as the mathematical focus that leads to theoretical guarantees. This includes methods for formal modeling, hypothesis tests, uncertainty quantification, and statistical inference. Of particular interest are also established statistical frameworks to handle causality or data deficiencies such as dependence, missingness, biases, or confounding. This article summarizes the findings of a discussion workshop on the topic that was held in June 2023 in Hannover, Germany. The discussion centered around the following questions: How must statistics be set up so that it can contribute (more) to modern data science? In which direction should it develop further? Which strengths can already be used now? What conditions must be created so that this can succeed? What can be done to arrive at a common language? What is the added value of formal modeling, inference, and the mathematical perspective taken in statistics?
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Harvard Data Science Review
ISSN
2688-8513
e-ISSN
2644-2353
Volume of the periodical
7
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
60
Pages from-to
ufaltur6
UT code for WoS article
001520662400001
EID of the result in the Scopus database
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