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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 Market
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Orientador(es)
Resumo(s)
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.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
Palavras-chave
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