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The Development of Educational Competences for Romanian Students in the Context of the Evolution of Data Science and Artificial Intelligence

Author

Listed:
  • Giani Ionel Gradinaru

    (Bucharest University of Economic Studies)

  • Vasile Dinu

    (Bucharest University of Economic Studies, Bucharest, Romanian and Academy of Scientists, Bucharest, Romania)

  • Catalin-Laurentiu Rotaru

    (Bucharest University of Economic Studies)

  • Andreea Toma

    (Bucharest University of Economic Studies)

Abstract

The study explores key academic competencies and professional skills in data science in the context of the development of artificial intelligence, highlighting their importance in the business environment. Using the “2022 Stack Overflow Annual Developer Survey” dataset and machine learning methods such as principal component analysis, K-means clustering, and logistic regression, professional skills in science are analysed the data. The research targets the distribution of jobs in the field, the level of experience, the languages and analysis programs used, the support offered by companies, and the dynamics of data science teams, as well as the impact that artificial intelligence has on the field. With their help, a comprehensive understanding of the impact of academic training on career opportunities in the field of data science is provided, contributing to the development of the profile of the qualified specialist in this field. The research also provides relevant pointers and recommendations for enhancing the skills required in data science in order to outline a skilled profile and fulfil the demands of the business environment in a world dominated by data analytics and artificial intelligence. By including academic skills in the process of training data science specialists, the research brings innovation and highlights the skills needed to be trained in the academic field to facilitate the employment of graduates in specific fields of data science. This aspect is significant because, in practice, it has been observed that most specialists working in data science rely on independent learning rather than skills acquired in the academic field.

Suggested Citation

  • 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.
    • Handle: RePEc:aes:amfeco:v:26:y:2024:i:65:p:14
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    References listed on IDEAS

    as
    1. J. Hardin & R. Hoerl & Nicholas J. Horton & D. Nolan & B. Baumer & O. Hall-Holt & P. Murrell & R. Peng & P. Roback & D. Temple Lang & M. D. Ward, 2015. "Data Science in Statistics Curricula: Preparing Students to “Think with Data”," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 343-353, November.
    2. 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.
    3. Stephanie C. Hicks & Rafael A. Irizarry, 2018. "A Guide to Teaching Data Science," The American Statistician, Taylor & Francis Journals, vol. 72(4), pages 382-391, October.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    data science; artificial intelligence; academic skills; professional skills.;
    All these keywords.

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

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