IDEAS home Printed from https://ideas.repec.org/a/aes/amfeco/v24y2022i60p464.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Aiting Xu & Yuchen Wu & Feina Meng & Shengying Xu & Yuhan Zhu, 2022. "Knowledge and Skill Sets for Big Data Professions: Analysis of Recruitment Information Based on The Latent Dirichlet Allocation Model," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 24(60), pages 464-464, April.
    • Handle: RePEc:aes:amfeco:v:24:y:2022:i:60:p:464
    as

    Download full text from publisher

    File URL: http://www.amfiteatrueconomic.ro/temp/Article_3112.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Giani Ionel Gradinaru & Vasile Dinu & Catalin-Laurentiu Rotaru & Andreea Toma, 2024. "The Development of Educational Competences for Romanian Students in the Context of the Evolution of Data Science and Artificial Intelligence," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(65), pages 1-14, February.

    More about this item

    Keywords

    Big Data professions; training system; matching degree; PDCA; LDA;
    All these keywords.

    JEL classification:

    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • J4 - Labor and Demographic Economics - - Particular Labor Markets
    • M1 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aes:amfeco:v:24:y:2022:i:60:p:464. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Valentin Dumitru (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.