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This bibliometric analysis explores the landscape of research in Data Science and Big Data Analytics over the period from 2010 to March 2024. Leveraging advanced bibliometric techniques, including data collection from Scopus, data screening, preprocessing, and analysis using VOSviewer, Bibliometric of R package, and Microsoft Excel, this study aims to identify key trends, patterns, and dynamics within the field. The analysis encompasses document types, publication and citation trends, contributing countries, influential authors and sources, keyword co-occurrence networks, and influential affiliations. The findings provide valuable insights into the scholarly discourse, collaboration networks, and emerging research directions in Data Science and Big Data Analytics, facilitating evidence-based decision-making and fostering innovation in the field.
Nageye et al. (Fri,) studied this question.