THE TALENT DEMAND STRUCTURE OF BIG DATA MANAGEMENT AND APPLICATION MAJOR UNDER THE BACKGROUND OF NEW-QUALITY PRODUCTIVE FORCES
Keywords:
New-quality productive forces, Big data management and application, Talent demand structureAbstract
With the accelerated development of new-quality productive forces, the talent demand structure for the Big Data Management and Application major is being reshaped. Based on 2403 valid job postings collected from mainstream recruitment platforms and surveys on graduates’ professional competencies, this paper adopts statistical analysis and the LDA (Latent Dirichlet Allocation) topic model to explore the characteristics of industrial talent demand. The study finds that bachelor’s degree serves as the mainstream academic threshold for the industry, and talent requirements are shifting from single-task technical execution toward compound "technology-plus-business" capabilities. Accordingly, this paper proposes constructing a curriculum system by leveraging the interdisciplinary strengths of economics and management.References
[1] Guo J L. Research on the training mode of innovative talents in big data technology majors in higher vocational colleges based on the integration of CDIO and BAG concepts. The Theory and Practice of Innovation and Entrepreneurship, 2026, 9(15): 80-83.
[2] Ye G H, Cao G H, Xia L X. Research on the joint construction path of Big Data Management and Application major among Chinese university information-management schools. Journal of the China Society for Scientific and Technical Information, 2023, 42(02): 231-240.
[3] Sun X, Yu Y W. Research on talent-training and practical-training modes for Big Data Management and Application major from the finance-economics perspective: a case study of Jilin University of Finance and Economics. Journal of Jilin Provincial Institute of Education, 2024, 40(10): 113-117.
[4] Sun Z, Li J X, Lu X M, et al. Analysis of competency requirements for Big Data Management and Application major and optimization strategies for talent cultivation: text mining based on recruitment information. Digital Library Forum, 2026: 1-10. https://link.cnki.net/urlid/11.5359.G2.20260921.1416.002.
[5] Grootendorst M. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794, 2022.
[6] Li L L, Jiao J L, Yang R R. Research on curriculum-system construction for Big Data Management and Application major under the emerging liberal-arts initiative. Library Science Research, 2023(10): 2-12.
[7] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993-1022.
[8] Li Y, Huang T, Li X. Training path of big data management and application talents based on BERTopic-TOPSIS model. PLoS ONE, 2025, 20(12): e0334127.