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December 4, 2025PLoS ONE2 citationsOpen Access

Training path of big data management and application talents based on BERTopic-TOPSIS model

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YLYan LiTHTao HuangXLXiang Li

Key Points

  • Significant misalignment was found between 52% of programs aiming for data application ability and only 11% specifying measurable skills.
  • Content analysis and social network analysis identified key competencies in job postings related to big data careers.
  • Observational analysis across job postings and training programs informed three primary pathways: data management, data analysis, and data platform development.
  • The findings suggest optimization of training objectives and course requirements, especially within leading institutions such as Peking University.

Abstract

This study examines the discrepancy between big data talent training and industry demand. The study analyzed 85 training programs and over 10,000 job postings from two job boards in China (51job and Zhaopin). Using content analysis, social network analysis, and the BERTopic-TOPSIS model, it mined implicit information from training programs and labeled key competencies in job descriptions. A key finding was a significant supply-demand misalignment: while “data application ability” was a stated goal in 52% of programs, only 11% of graduation requirements specified concrete, measurable skills to achieve it. The study identified three primary employment pathways for big data management and application majors: data management, data analysis, and data platform development. Institutions such as Peking University and Hefei University of Technology were identified as best practices. The study then delineated a cultivation path for the major by integrating the characteristics of these employment pathways, and optimised general knowledge and compulsory courses, core courses, graduation requirements, and the cultivation objectives of the major.

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Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/6930e8b6ea1aef094cca3079https://doi.org/10.1371/journal.pone.0334127
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