Identification and comparative analysis of the skills structure of the data analyst profession in Russia

Skhvediani, Angi, Sosnovskikh, Sergey ORCID: https://orcid.org/0000-0002-3744-740X, Rudskaia, Irina ORCID: https://orcid.org/0000-0002-9953-6619 and Kudryavtseva, Tatiana ORCID: https://orcid.org/0000-0003-1403-3447 (2022) Identification and comparative analysis of the skills structure of the data analyst profession in Russia. Journal of Education for Business, 97 (5). pp. 295-304. ISSN 0883-2323

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Official URL: http://dx.doi.org/10.1080/08832323.2021.1937018

Abstract

The development of digital technologies has created a market need for specialists working with the big data that is necessary for making management decisions. This study aims to identify the skills structure of the data analyst profession (DAP) in Russia. The authors used a program code written in Python to examine relevant vacancies extracted from a recruitment website and employed a social network analysis method to identify skill clusters. Findings suggest that the DAP consists of predominately hard skills, that is, specialists must have the technical skills required to collect and process information. These results could be used to develop a Higher Education curriculum in data analysis.

Item Type:	Article (Article)
Peer-reviewed:	Yes
Date Deposited:	11 Mar 2024 15:32
Publisher:	Taylor & Francis
Additional Information:	This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Education for Business on 16 June 2021, available at: https://doi.org/10.1080/08832323.2021.1937018 It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Divisions:	Faculties > Business and Law
Subject terms:	1302 Curriculum and Pedagogy, 1503 Business and Management
URI:	https://mmu-uat.leaf.cosector.com/id/eprint/634051
DOI:	https://doi.org/10.1080/08832323.2021.1937018
ISSN	0883-2323
e-ISSN	1940-3356

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