Data Science Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour Market

Detalhes bibliográficos
Autor(a) principal: Nunes, Filipa João Marques de Abreu e Santos
Data de Publicação: 2024
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/174756
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
id RCAP_614c88bcf9cada112185aabf3a6e1703
oai_identifier_str oai:run.unl.pt:10362/174756
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Data Science Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour MarketCurricula DevelopmentCurricula FrameworkCurriculumData ScienceEducationEmployabilityHigher EducationLabour MarketSDG 4 - Quality educationSDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructureSDG 11 - Sustainable cities and communitiesDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis thesis addresses the need for structured curricula (re)design in Higher Education Data Science programs through a proposed framework. By synthesizing insights from extensive primary and secondary sources, this research raises awareness on the urgent need to update Higher Education Data Science curricula. It highlights how urgently old theoretical approaches must give way to a more balanced framework that places an emphasis on project-based learning, real-world professional contexts, soft skill development, and practical preparedness. This study proposes a comprehensive five-stage methodology for Data Science curricula (re)design, progressing through stages focused on defining educational objectives, student outcomes, gathering external input, and curriculum development, to ensure alignment with both educational standards and the Data Science industry demands. Feedback from stakeholders underscores the framework's effectiveness in fostering curriculum relevancy, academic rigor, and industry preparedness. The methodology emphasizes iterative refinement and strategic goal setting, culminating in a robust validation and implementation phase. By providing a systematic strategy that can be easily adjusted to different institutional contexts, this thesis improves the quality of education and graduates' preparedness for the fast-paced area of Data Science.Malta, Pedro Manuel Carqueijeiro Espiga da MaiaRUNNunes, Filipa João Marques de Abreu e Santos2024-10-292027-10-29T00:00:00Z2024-10-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/174756enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-11-11T01:42:57Zoai:run.unl.pt:10362/174756Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-11-11T01:42:57Repositório 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 Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour Market
title Data Science Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour Market
spellingShingle Data Science Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour Market
Nunes, Filipa João Marques de Abreu e Santos
Curricula Development
Curricula Framework
Curriculum
Data Science
Education
Employability
Higher Education
Labour Market
SDG 4 - Quality education
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
SDG 11 - Sustainable cities and communities
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Data Science Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour Market
title_full Data Science Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour Market
title_fullStr Data Science Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour Market
title_full_unstemmed Data Science Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour Market
title_sort Data Science Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour Market
author Nunes, Filipa João Marques de Abreu e Santos
author_facet Nunes, Filipa João Marques de Abreu e Santos
author_role author
dc.contributor.none.fl_str_mv Malta, Pedro Manuel Carqueijeiro Espiga da Maia
RUN
dc.contributor.author.fl_str_mv Nunes, Filipa João Marques de Abreu e Santos
dc.subject.por.fl_str_mv Curricula Development
Curricula Framework
Curriculum
Data Science
Education
Employability
Higher Education
Labour Market
SDG 4 - Quality education
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
SDG 11 - Sustainable cities and communities
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Curricula Development
Curricula Framework
Curriculum
Data Science
Education
Employability
Higher Education
Labour Market
SDG 4 - Quality education
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
SDG 11 - Sustainable cities and communities
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2024
dc.date.none.fl_str_mv 2024-10-29
2024-10-29T00:00:00Z
2027-10-29T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/174756
url http://hdl.handle.net/10362/174756
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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
repository.mail.fl_str_mv mluisa.alvim@gmail.com
_version_ 1817548687153299456