Aim of the study: The article presents the results of a study that analyzed statistical data on the training of specialists by institutions of secondary professional and higher education who have the necessary competencies to work in the conditions of digitalization of the economy. The purpose of the study was to develop and test indices of personnel support for the digitalization of the economy, as well as to identify socio-economic factors that significantly affect the level of personnel support for the digital transformation of the economy.
Methodology: The study uses data from official statistical reports of the Russian
Federation. The proposed staffing indices were modeled as target functions that depend on socio-economic factors that characterize the development of the economy in different dimensions. At the same time, the indices themselves were calculated as values that correlate the parameters of the output of digital specialists and their demand in the economy.
Conclusion: The study compared statistical and neural network methods of data
modeling and their generalizing indexes. The analysis of the obtained regression models and sensitivity analysis of trained neural networks allowed us to evaluate their accuracy in predicting trends in the digital economy staffing and to identify factors that significantly affect the achievement of the goal of matching the output of specialists and the requests of economic sectors.