Publicação
Data Science Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour Market
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Malta, Pedro Manuel Carqueijeiro Espiga da Maia | |
| dc.contributor.author | Nunes, Filipa João Marques de Abreu e Santos | |
| dc.date.accessioned | 2024-11-07T12:00:44Z | |
| dc.date.embargo | 2027-10-29 | |
| dc.date.issued | 2024-10-29 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence | pt_PT |
| dc.description.abstract | This 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. | pt_PT |
| dc.identifier.tid | 203777360 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/174756 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Curricula Development | pt_PT |
| dc.subject | Curricula Framework | pt_PT |
| dc.subject | Curriculum | pt_PT |
| dc.subject | Data Science | pt_PT |
| dc.subject | Education | pt_PT |
| dc.subject | Employability | pt_PT |
| dc.subject | Higher Education | pt_PT |
| dc.subject | Labour Market | pt_PT |
| dc.subject | SDG 4 - Quality education | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.subject | SDG 11 - Sustainable cities and communities | pt_PT |
| dc.title | Data Science Curricula (re)design: A framework to achieve alignment between Higher Education Institutions and the needs of the Data Science Labour Market | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Gestão do Conhecimento e Inteligência de Negócio | pt_PT |