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Export Reference (APA) 
Strazzullo, S., Cortez, P. & Moro, S. (2024). Data science approaches for sustainable development. Expert Systems. 41 (7)
Export Reference (IEEE) 
S. Strazzullo et al.,  "Data science approaches for sustainable development", in Expert Systems, vol. 41, no. 7, 2024
Export BibTeX 
@null{strazzullo2024_1764939905945,
	year = "2024",
	url = "https://onlinelibrary.wiley.com/journal/14680394"
}
Export RIS 
TY  - GEN
TI  - Data science approaches for sustainable development
T2  - Expert Systems
VL  - 41
AU  - Strazzullo, S.
AU  - Cortez, P.
AU  - Moro, S.
PY  - 2024
SN  - 0266-4720
DO  - 10.1111/exsy.13613
UR  - https://onlinelibrary.wiley.com/journal/14680394
AB  - In today's fast-evolving world, the intersection of technological innovation and sustainable development has emerged as a beacon of hope for addressing global challenges. The application of data science in this context represents a powerful and transformative force, amplifying our capabilities to navigate complex societal and environmental issues. This Special Issue of Expert Systems on ‘Data Science for Sustainable Development’ is a testament to the dynamic and promising fusion of these disciplines. This last analysis examines how data science tools contribute to achieving the objectives established in September 2015 by the United Nations General Assembly, UN, within the document known as the 2030 Agenda for Sustainable Development. The objective of the Agenda is to achieve a level of growth for all countries, such as guaranteeing a sustainable future, through objectives that can be summarized in three main groups, namely the environmental, economic, and social, in the perspective of the protection of the planet. These are ambitious objectives that require adopting measures aimed at their fulfilment. The governments of the G20 were the first to represent the forerunners for the realization of this development. Given the interdisciplinary nature of data science, this can be applied to the implementation and monitoring of the achievement of the 17 objectives. The latter includes methods of collection, pre-processing, extraction of meaning/useful characteristics, methods of data exploration and predictive models.
ER  -