Bibliometric Analysis of Data Science Research: A Decade of Insights from Web of Science

Knowledge and insight extraction from large datasets has been revolutionised by the rapid development and expansion of data science in recent years. Bibliometric analysis is an effective method for studying research tendencies, significant authors, institutions, and major areas of attention to better comprehend the development of this field as a whole. Here, employing Web of Science's massive database, we give a bibliometric study of research in data science conducted over the previous decade. We use a number of bibliometric indicators and visualisation tools to try and spot trends, new areas of study, and key players in the field. Publication output, citation patterns, cooperation networks, and research hubs are just few of the many aspects we consider in our analyses. Our goal in conducting this extensive analysis is to illuminate the most important trends that have formed the field of data science over the last decade and give novel perspectives on the discipline's history, development, and future. Researchers, practitioners, and policymakers may use this study as a starting point for thinking critically about the ever-changing environment of data science and locating promising new areas for investigation and cooperation.