Технологическое целеполагание в рамках новой образовательной области: «Большие данные» для системы профессиональной подготовки будущего экономиста

Abstract

A weighty argument in favor of including the new educational area «Big Data» in the practice of professional training of the future economist is the competence in the field of building adequate predictive models, which is in demand in the modern labor market. Indeed, any leader is interested in improving the quality of the decisions made. This interest increases in conditions of sanctions pressure and post-pandemic restrictions, in difficult socio-economic conditions, when most of the resources are limited, the previously identified cause-and-effect relationships lose their relevance and the responsibility for decisions is significantly increased. Features of the implementation of the technological approach to disclosing the content of the new educational area «Big Data» in the system of professional training of the future economist is presented in this article as follows: firstly, in the form of a system of micro-goals at the basic level, and secondly, in the form of a system of micro-goals at an advanced level. Thus, within the framework of the technological goal-setting of the content of the new educational field, the principle of variability of the professional training of the future economist is implemented. Substantively presented in the article micro-goals cover various issues of using quantitative methods, mathematical and computational modeling. In addition, the formulations of micro-goals include requirements for the development of new tools that support big data analysis. Note that the implementation of technological goal-setting is necessary to strengthen the applied orientation of the training of a future economist, allows us to make a methodological emphasis on applied problems of socio-economic topics, the methods of solving which are in demand in future professional activities. The material of the article can be useful to teachers of the higher school of economics, as well as to anyone interested in modern methodological approaches to structuring educational content and achievements in the field of big data.