Is higher education adequately preparing graduates for the new Data professions?

Daniel Lang ()
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Daniel Lang: LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], IMT-BS - TIM - Département Technologies, Information & Management - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris]

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Abstract: The advent of Artificial Intelligence (AI) is bringing about profound changes, particularly in terms of the new professions involved. These technologies are creating a need for new skills specific to Data. The aim of our contribution is to identify the skills gaps between the higher education courses that train students in the fields of Artificial Intelligence and Data, and the needs of the job market. To do this, we have - extracting Data training programs from the websites of higher education establishments - scraping a job vacancy website. We then developed an analysis based on a text mining approach, using Python code for natural language processing. Our in-depth analysis using natural language processing algorithms provides a better understanding of the components needed to best train new generations in the new professions driven by Data. This study can help shape educational programs to meet the demands of the new professions market.

Keywords: Data Science; Labour Market; Higher Education; Text Mining; Web Scraping (search for similar items in EconPapers)
Date: 2024-08-15
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Published in AMCIS 2024 : Americas Conference on Information Systems, Association for Information Systems (AIS), Aug 2024, Salt Lake City, United States

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