Treffer: Cooking Up Knowledge From Big Data Using Data Science

Title:
Cooking Up Knowledge From Big Data Using Data Science
Authors:
Contributors:
Génétique Animale et Biologie Intégrative (GABI), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Source:
Frontiers for Young Minds. 9(11)
Publisher Information:
CCSD; Frontiers Media, 2021.
Publication Year:
2021
Collection:
collection:AGROPARISTECH
collection:UNIV-PARIS-SACLAY
collection:AGREENIUM
collection:INRAE
collection:UNIVERSITE-PARIS-SACLAY
collection:GENETIQUE_ANIMALE
collection:GS-BIOSPHERA
collection:GS-LIFE-SCIENCES-HEALTH
collection:GABI
collection:SAPS
collection:TEST-COLLECTION-INRAE
collection:INRAE-TRANSFERT
collection:APT_TEST
Original Identifier:
HAL: hal-03485735
Document Type:
Zeitschrift article<br />Journal articles
Language:
English
ISSN:
2296-6846
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.3389/frym.2021.632923
DOI:
10.3389/frym.2021.632923
Accession Number:
edshal.hal.03485735v1
Database:
HAL

Weitere Informationen

Data collected in very large quantities are called big data, and big data has changed the way we think about and answer questions in many different fields, like weather forecasting and biology. With all this information available, we need computers to help us store, process, analyze, and understand it. Data science combines tools from fields like statistics, mathematics, and computer science to find interesting patterns in big data. Data scientists write step-by-step instructions called algorithms to teach computers how to learn from data. To help computers understand these instructions, algorithms must be translated from the original question asked by a data scientist into a programming language—and the results must be translated back, so that humans can understand them. That means that data scientists are data detectives, programmers, and translators all in one!