Scientific Exploration: Bibliometric Analysis of the Dynamics of Data Science Education Development based on Scopus (2013-2023) - Saifudin, Ikhwan; Maksum, Muh. Nur Rochim; Jinan, Muthohharun; Andhim, Muk; Apriantoro, Muhamad Subhi (Proceeding ISETH (International Summit on Science, Technology, and Humanity), 2023)
This study aims to map the development and direction of Data Science Education research in Scopus indexed publications. This research uses bibliometric analysis techniques to explore all publications indexed in the Scopus database on Data Science Education from 2013 to 2023. The data were analyzed using Excel and R/R-Studio. VOSviewer was used to perform a visual analysis of the simultaneous occurrence of keywords and document citations. The authors found 1,987 publications that matched the defined function, subject, and criteria. The results of this study show an annual growth rate of 4%, with the most publications on Data Science Education in 2017 and 2018. The United States is the country that contributes the most publications with affiliation from Tehran University Of medical Sciences. Brownel, S.E. became the most productive author in the theme of Data Science Education Bibliometric analysis conducted is limited to Scopus data. The keywords such as Covid -19, Professional Development, Sustainability, E-learning, and Academic Achievement are keywords that are still blurry or rarely researched. Other national and international databases were not taken into account in this study. This research presents a brief overview of the literature that can be accessed by researchers working in the field of Data Science Education and provides recommendations for future research.