Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Hidri, Adela | Mkhinini Gahar, Raniab; * | Sassi Hidri, Minyara
Affiliations: [a] Computer Department, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia | [b] OASIS Research Laboratory, National Engineering School of Tunis, University of Tunis El Manar, Tunis, Tunisia
Correspondence: [*] Corresponding author: Rania Mkhinini Gahar, OASIS Research Laboratory, National Engineering School of Tunis, University of Tunis El Manar, Tunis, Tunisia. E-mail: [email protected].
Abstract: Distinguishing between roles like Data Scientist, Data Engineer, Data Analyst, and Business Intelligence Developer can be challenging, as there can be overlap in responsibilities, focuses, and skill sets across these positions. By understanding these distinctions, job seekers can better align their skills and interests with the specific requirements and factors of each role, thereby increasing their chances of finding a fulfilling career in the data field. To address what factors distinguish these positions, we developed machine learning models capable of clarifying the distinctions among these positions based on relevant features extracted from the dataset. The proposed learning models leverage relevant features extracted from the dataset to differentiate between roles accurately. Factors such as technical skills, programming languages, educational background, work experience, and certifications likely play crucial roles in distinguishing between these positions. By incorporating these features into the models, they can effectively identify patterns and characteristics unique to each role. The high accuracy (approximately 99%) achieved by these models not only validates their effectiveness but also underscores the importance of understanding the nuances and specific requirements of each role within the data field. Armed with this knowledge, both job seekers and employers can make more informed decisions when it comes to hiring, career planning, and talent acquisition.
Keywords: Machine learning, Data Scientists, Data Engineers, Data Analysts, Business Intelligence Developers
DOI: 10.3233/IDT-240509
Journal: Intelligent Decision Technologies, vol. 18, no. 3, pp. 2161-2176, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]