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

Bibliographic Details
Main Author: Nunes, Filipa João Marques de Abreu e Santos
Publication Date: 2024
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/174756
Summary: 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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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/10362/174756TID:203777360enginfo:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-01-13T01:41:47Zoai:run.unl.pt:10362/174756Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:12:58.646660Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
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
TID:203777360
url http://hdl.handle.net/10362/174756
identifier_str_mv TID:203777360
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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