Data Science Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour Market
Autor(a) principal: | |
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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 |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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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 |