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

Giani Ionel Gradinaru (), Vasile Dinu, Catalin-Laurentiu Rotaru and Andreea Toma
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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

The AMFITEATRU ECONOMIC journal, 2024, vol. 26, issue 65, 14

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.

Keywords: data science; artificial intelligence; academic skills; professional skills. (search for similar items in EconPapers)
JEL-codes: C49 I23 J23 M15 (search for similar items in EconPapers)
Date: 2024
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