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Thematic Specialization of Institutions with Academic Programs in the Field of Data Science

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  • Denis Gonzalez Argote

Abstract

Introduction: data science careers are on the rise due to the growing demand for technical skills in this area. Data science careers focus on collecting, organizing, and analyzing data to identify patterns and trends, which allows organizations to make informed decisions and develop effective solutions. Aim: to analyze the thematic specialization of institutions with academic programs in the area of data science. Methods: the Scopus database was used to conduct a bibliometric analysis aimed at examining the thematic specialization of institutions with academic programs in the field of data science. SciVal, a bibliometric analysis tool, was employed to extract the relevant data. The study period ranged from 2012 to 2021. Results: nine higher education institutions were found to offer undergraduate or graduate degrees in the field of data science. There was no correlation found between RSI and Field-Weighted Citation Impact (r=0,05355; P=0,8912; 95%CI: -0,6331 to 0,6930). Therefore, it cannot be claimed that specialization in the subject area studied influences the greater impact of research. On the other hand, recent accreditation did not influence greater specialization (r=0,1675; P=0,6667; 95%CI: -0,5588 to 0,7484). Additionally, no differences were found regarding academic level. Conclusions: the analysis of the thematic specialization of institutions with academic programs in the field of data science shows low scientific production in this field. Moreover, more than half of the analyzed higher education institutions have thematic specialization below the global average. This suggests that there is still a long way to go for these institutions to achieve adequate specialization and compete internationally in the field of data science

Suggested Citation

  • Denis Gonzalez Argote, 2023. "Thematic Specialization of Institutions with Academic Programs in the Field of Data Science," Data and Metadata, AG Editor, vol. 2, pages 1-24.
    • Handle: RePEc:dbk:datame:v:2:y:2023:i::p:24:id:1056294dm202324
    DOI: 10.56294/dm202324
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