Unlocking the Multidisciplinary Potential of Data Science: Insights from Apriori Analysis

Abstract

Data science holds paramount significance for the progress of technology and science. Consequently, it is imperative to discern the existing studies in data science and identify areas where research is deficient. For this reason, this study aims to identify, analyse other fields where researchers work in data science, and provide guidance for future research endeavours. The application of apriori analysis to two distinct data groups utilising the R Studio program is expounded in this article. The first data group comprises 2262 articles from SSCI, SCI, and E-SCI indexed journals, sourced from the Web of Science database using the keyword "data science." The second dataset is derived from a list of over 15,000 cited authors (316 authors) specialising in data science on Google Scholar. The study encompasses a total of 2262 articles and data from 316 authors. The articles encompass 6533 unique keywords. Employing apriori analysis, a data mining method, on the acquired datasets involves using support, confidence, and lift values to ascertain association rule outputs. The Apriori analysis results indicate that data science is pivotal in decision and policymaking, developing learning methods for educators, breast cancer treatment, and genetic science in the health domain. Furthermore, data science is significant in diverse fields such as cosmology and ecology. This outcome reaffirms the interdisciplinary nature of data science.