Statistical Data Research on Staff Training for the Digital Economy in the Russian Federation
The article analyzes the statistical data relating to training specialists for digitalized economy by secondary vocational and higher education institutions. The purpose of the study was to develop and test personnel support indices for digitalization of the economy, as well as to identify social an...
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| Format: | Article |
| Language: | English |
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Moscow Polytechnic University
2021-11-01
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| Series: | Высшее образование в России |
| Subjects: | |
| Online Access: | https://vovr.elpub.ru/jour/article/view/3111 |
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| author | Yu. V. Frolov T. M. Bosenko |
| author_facet | Yu. V. Frolov T. M. Bosenko |
| author_sort | Yu. V. Frolov |
| collection | DOAJ |
| description | The article analyzes the statistical data relating to training specialists for digitalized economy by secondary vocational and higher education institutions. The purpose of the study was to develop and test personnel support indices for digitalization of the economy, as well as to identify social and economic factors that significantly affect the level of personnel support for the processes of digital transformation of the economy. The authors applied data from the official statistical reporting of the Russian Federation. The proposed staffing indices were modeled as objective functions depending on socio-economic factors characterizing the development of the economy in different dimensions. At the same time, the indices themselves were calculated as values in which the parameters of the output of digital specialists and their relevance in the economy were correlated. In the course of the study, a comparison of statistical and neural network data modeling methods and generalizing indices was performed. An analysis of the obtained regression models and an analysis of the sensitivity of trained neural networks made it possible to evaluate their accuracy in predicting the trends in the staffing of the digital economy and to identify factors that significantly affect the achievement of the goal of matching the output of specialists and the demands of economic sectors. |
| format | Article |
| id | doaj-art-e79c8d67a2314a439869e644155affda |
| institution | Kabale University |
| issn | 0869-3617 2072-0459 |
| language | English |
| publishDate | 2021-11-01 |
| publisher | Moscow Polytechnic University |
| record_format | Article |
| series | Высшее образование в России |
| spelling | doaj-art-e79c8d67a2314a439869e644155affda2025-02-01T13:14:30ZengMoscow Polytechnic UniversityВысшее образование в России0869-36172072-04592021-11-013011294110.31992/0869-3617-2021-30-11-29-411852Statistical Data Research on Staff Training for the Digital Economy in the Russian FederationYu. V. Frolov0T. M. Bosenko1Moscow City Pedagogical UniversityMoscow City Pedagogical UniversityThe article analyzes the statistical data relating to training specialists for digitalized economy by secondary vocational and higher education institutions. The purpose of the study was to develop and test personnel support indices for digitalization of the economy, as well as to identify social and economic factors that significantly affect the level of personnel support for the processes of digital transformation of the economy. The authors applied data from the official statistical reporting of the Russian Federation. The proposed staffing indices were modeled as objective functions depending on socio-economic factors characterizing the development of the economy in different dimensions. At the same time, the indices themselves were calculated as values in which the parameters of the output of digital specialists and their relevance in the economy were correlated. In the course of the study, a comparison of statistical and neural network data modeling methods and generalizing indices was performed. An analysis of the obtained regression models and an analysis of the sensitivity of trained neural networks made it possible to evaluate their accuracy in predicting the trends in the staffing of the digital economy and to identify factors that significantly affect the achievement of the goal of matching the output of specialists and the demands of economic sectors.https://vovr.elpub.ru/jour/article/view/3111digitalizationdigital specialistsstatistical socio-economic datastaffing indicesinstitutions of secondary vocational educationhigher vocational educationregression modelsneural networks |
| spellingShingle | Yu. V. Frolov T. M. Bosenko Statistical Data Research on Staff Training for the Digital Economy in the Russian Federation Высшее образование в России digitalization digital specialists statistical socio-economic data staffing indices institutions of secondary vocational education higher vocational education regression models neural networks |
| title | Statistical Data Research on Staff Training for the Digital Economy in the Russian Federation |
| title_full | Statistical Data Research on Staff Training for the Digital Economy in the Russian Federation |
| title_fullStr | Statistical Data Research on Staff Training for the Digital Economy in the Russian Federation |
| title_full_unstemmed | Statistical Data Research on Staff Training for the Digital Economy in the Russian Federation |
| title_short | Statistical Data Research on Staff Training for the Digital Economy in the Russian Federation |
| title_sort | statistical data research on staff training for the digital economy in the russian federation |
| topic | digitalization digital specialists statistical socio-economic data staffing indices institutions of secondary vocational education higher vocational education regression models neural networks |
| url | https://vovr.elpub.ru/jour/article/view/3111 |
| work_keys_str_mv | AT yuvfrolov statisticaldataresearchonstafftrainingforthedigitaleconomyintherussianfederation AT tmbosenko statisticaldataresearchonstafftrainingforthedigitaleconomyintherussianfederation |