Content area

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.

Details

1009240
Location
Title
Training path of big data management and application talents based on BERTopic-TOPSIS model
Publication title
PLoS One; San Francisco
Volume
20
Issue
12
First page
e0334127
Number of pages
27
Publication year
2025
Publication date
Dec 2025
Section
Research Article
Publisher
Public Library of Science
Place of publication
San Francisco
Country of publication
United States
e-ISSN
19326203
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Milestone dates
2025-02-05 (Received); 2025-09-23 (Accepted); 2025-12-01 (Published)
ProQuest document ID
3278201833
Document URL
https://www.proquest.com/scholarly-journals/training-path-big-data-management-application/docview/3278201833/se-2?accountid=208611
Copyright
© 2025 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-12-02
Database
ProQuest One Academic