Challenges and Opportunities for Statistics in the Era ofData Science

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Challenges and Opportunities for Statistics in the Era ofData Science

Popis výsledku v původním jazyce

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?

Název v anglickém jazyce

Challenges and Opportunities for Statistics in the Era ofData Science

Popis výsledku anglicky

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?

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Harvard Data Science Review

ISSN

2688-8513

e-ISSN

2644-2353

Svazek periodika

7

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

60

Strana od-do

ufaltur6

Kód UT WoS článku

001520662400001

EID výsledku v databázi Scopus

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