A decade of data science research: insights from a bibliometric study

Data science has rapidly evolved over the past decade, emerging as a transformative discipline at the intersection of computational methods, machine learning, and big data analytics. The exponential growth of publications highlights the need for a systematic evaluation of global research trends. This study aims to map and analyze a decade of data science research (2015–2025), identifying key contributors, thematic trends, and collaboration patterns, while applying classical bibliometric laws such as Lotka’s and Bradford’s. Scientometric approach was employed using data retrieved from the Scopus database, covering 27,108 documents. Tools such as Biblioshiny and VOSviewer facilitated the analysis of publication trends, author productivity, keyword co-occurrence, and country collaborations. Metrics like Relative Growth Rate (RGR), Doubling Time (DT), and thematic mapping provided in-depth insights. Findings reveal a strong upward trajectory in publications, with the USA, China and India leading global research output. Zhang, Yilong and Wang, Jianyu emerged as highly prolific authors, while “machine learning” and “artificial intelligence” dominated as research themes. Collaboration networks demonstrate increasing internationalization, though disparities remain in global participation. This study uniquely integrates classical bibliometric laws with advanced visualizations, offering a holistic perspective on data science research dynamics and identifying emerging frontiers for future exploration.