Knowledge and Skill Sets for Big Data Professions: Analysis of Recruitment Information Based on The Latent Dirichlet Allocation Model

Aiting Xu, Yuchen Wu, Feina Meng, Shengying Xu and Yuhan Zhu ()
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Aiting Xu: Zhejiang Gongshang University, Zhejiang, China
Yuchen Wu: Zhejiang Gongshang University, Zhejiang, China
Feina Meng: Zhejiang Gongshang University, Zhejiang, China
Shengying Xu: Zhejiang Gongshang University, Zhejiang, China
Yuhan Zhu: Zhejiang Gongshang University, Zhejiang, China

The AMFITEATRU ECONOMIC journal, 2022, vol. 24, issue 60, 464

Abstract: Universities, enterprises, and students are the key subjects in the talent training system of Big Data. This study used text mining, interviews, questionnaires, and other methods to analyze the characteristics and deficiencies of Chinese universities in the training of Big Data talents, the requirements of enterprises on the professional quality of big data talents, and the cognition of students on the ability of big data talents. Furthermore, this study used the theory of plan-action-inspection-action cycle to evaluate the talent cultivation and quality management system of Big Data in China. The results showed that employees with rich professional background and proficiency in multiple programming languages are more favored by enterprises in recruitment. From the perspective of students, 83% of students hope that universities will implement the multi-disciplinary training model. From the perspective of talent training, more Chinese universities should open up the mode of interdisciplinary talent training. The results, based on pair-to-body comparisons, showed that students and businesses differ in skills. Secondly, most of the graduates think that the training of talents in universities needs to be improved. Furthermore, the training system of most universities is not suitable for the versatile talents that today’s enterprises need.

Keywords: Big Data professions; training system; matching degree; PDCA; LDA (search for similar items in EconPapers)
JEL-codes: J2 J4 M1 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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