Data Science Training for Official Statistics

Detalhes bibliográficos
Autor(a) principal: Ashofteh, Afshin
Data de Publicação: 2021
Outros Autores: Bravo, Jorge M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/124707
Resumo: Ashofteh, A., & Bravo, J. M. (2021). Data Science Training for Official Statistics: a New Scientific Paradigm of Information and Knowledge Development in National Statistical Systems. Statistical Journal of the IAOS, 37(3), 771 – 789. https://doi.org/10.3233/SJI-210841
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spelling Data Science Training for Official Statisticsa New Scientific Paradigm of Information and Knowledge Development in National Statistical SystemsData scienceMachine learningBig DataInformation managementStatistical engineeringOfficial statisticsStatistical literacyManagement Information SystemsEconomics and EconometricsStatistics, Probability and UncertaintyAshofteh, A., & Bravo, J. M. (2021). Data Science Training for Official Statistics: a New Scientific Paradigm of Information and Knowledge Development in National Statistical Systems. Statistical Journal of the IAOS, 37(3), 771 – 789. https://doi.org/10.3233/SJI-210841The ability to incorporate new and Big Data sources and to benefit from emerging technologies such as Web Technologies, Remote Data Collection methods, User Experience Platforms, and Trusted Smart Statistics will become increasingly important in producing and disseminating official statistics. The skills and competencies required to automate, analyse, and optimize such complex systems are often not part of the traditional skill set of most National Statistical Offices. The adoption of these technologies requires new knowledge, methodologies and the upgrading of the quality assurance framework, technology, security, privacy, and legal matters. However, there are methodological challenges and discussions among scholars about the diverse methodical confinement and the wide array of skills and competencies considered relevant for those working with big data at NSOs. This paper develops a Data Science Model for Official Statistics (DSMOS), graphically summarizing the role of data science in statistical business processes. The model combines data science, existing scientific paradigms, and trusted smart statistics, and develops around a restricted number of constructs. We considered a combination of statistical engineering, data engineering, data analysis, software engineering and soft skills such as statistical thinking, statistical literacy and specific knowledge of official statistics and dissemination of official statistics products as key requirements of data science in official statistics. We then analyse and discuss the educational requirements of the proposed model, clarifying their contribution, interactions, and current and future importance in official statistics. The DSMOS was validated through a quantitative method, using a survey addressed to experts working at the European statistical systems. The empirical results show that the core competencies considered relevant for the DSMOS include acquisition and processing capabilities related to Statistics, high-frequency data, spatial data, Big Data, and microdata/nano-data, in addition to problem-solving skills, Spatio-temporal modelling, machine learning, programming with R and SAS software, Data visualisation using novel technologies, Data and statistical literacy, Ethics in Official Statistics, New data methodologies, New data quality tools, standards and frameworks for official statistics. Some disadvantages and vulnerabilities are also addressed in the paper.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAshofteh, AfshinBravo, Jorge M.2021-09-17T01:05:55Z2021-09-142021-09-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article19application/pdfhttp://hdl.handle.net/10362/124707eng1874-7655PURE: 31625010https://doi.org/10.3233/SJI-210841info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:06:00Zoai:run.unl.pt:10362/124707Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:45:33.647323Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Data Science Training for Official Statistics
a New Scientific Paradigm of Information and Knowledge Development in National Statistical Systems
title Data Science Training for Official Statistics
spellingShingle Data Science Training for Official Statistics
Ashofteh, Afshin
Data science
Machine learning
Big Data
Information management
Statistical engineering
Official statistics
Statistical literacy
Management Information Systems
Economics and Econometrics
Statistics, Probability and Uncertainty
title_short Data Science Training for Official Statistics
title_full Data Science Training for Official Statistics
title_fullStr Data Science Training for Official Statistics
title_full_unstemmed Data Science Training for Official Statistics
title_sort Data Science Training for Official Statistics
author Ashofteh, Afshin
author_facet Ashofteh, Afshin
Bravo, Jorge M.
author_role author
author2 Bravo, Jorge M.
author2_role author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Ashofteh, Afshin
Bravo, Jorge M.
dc.subject.por.fl_str_mv Data science
Machine learning
Big Data
Information management
Statistical engineering
Official statistics
Statistical literacy
Management Information Systems
Economics and Econometrics
Statistics, Probability and Uncertainty
topic Data science
Machine learning
Big Data
Information management
Statistical engineering
Official statistics
Statistical literacy
Management Information Systems
Economics and Econometrics
Statistics, Probability and Uncertainty
description Ashofteh, A., & Bravo, J. M. (2021). Data Science Training for Official Statistics: a New Scientific Paradigm of Information and Knowledge Development in National Statistical Systems. Statistical Journal of the IAOS, 37(3), 771 – 789. https://doi.org/10.3233/SJI-210841
publishDate 2021
dc.date.none.fl_str_mv 2021-09-17T01:05:55Z
2021-09-14
2021-09-14T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/124707
url http://hdl.handle.net/10362/124707
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1874-7655
PURE: 31625010
https://doi.org/10.3233/SJI-210841
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 19
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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