Data science engineering: curricular management for responsible production and consumption
DOI:
https://doi.org/10.5281/zenodo.17186649Keywords:
responsible production and consumption, data science engineering, pattern categorization frameworkAbstract
Introduction: given the rapid pace of technological change, Data Science engineers will play a crucial role in driving sustainable innovation. By applying data analysis, machine learning, and artificial intelligence, they can optimize resource use, improve energy efficiency, and develop predictive models to help mitigate social and environmental risks. Objective: to integrate sustainability into business education, specifically in the context of the course “Fundamentals of Business Organization” in the Data Science degree at the Universitat Politècnica de València (UPV, Spain). Method: this qualitative and constructivist research is based on an educational action-research project in the FOE course. It analyzes transversal competencies and applies the Pattern Categorization Framework through practical activities, integrating sustainability into business analysis. A final activity is proposed that incorporates this tool into the course. Results: a methodology is designed to introduce “Social and Environmental Commitment” into the training of Data Science engineers. Conclusion: students will be able to address social, environmental, and economic challenges with ethics and professional responsibility, guided by the Sustainable Development Goals, particularly the goal of Responsible Production and Consumption.
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