The role of analysis and visualization technologies in the training of digital economists

this paper explores solutions to a pressing issue that arises during the training of economics students: the gap between the formal mastery of individual software products and graduates' ability to apply them comprehensively to solve professional problems. The aim of the study is to develop a methodology that will ensure the development of end-to-end data analysis competencies in future economists, integrated into the context of real economic activity. To achieve this goal, the following tasks were completed: analyze the requirements of professional standards for analytical skills; identify the didactic potential and area of responsibility of key software classes (spreadsheets, programming languages, BI platforms, cloud analytical environments); and design a system of interconnected practical assignments simulating the full work cycle of an analyst. The primary method was the design of an end-to-end educational case study, "Analysis of Retail Network Efficiency Factors". The practical value of the results lies in providing instructors with a ready-made model for modernizing their work programs. The developed set of tasks, which consistently involves MS Excel for initial analysis, Python for econometric modeling, Power BI for visualization, and Yandex Datalens for project consolidation, demonstrates the transformation of disparate technical skills into a conscious professional competency in demand in today's labor market.

digital competencies of an economist, data visualization, case study, practice-oriented learning, Python, Power BI, Yandex Datalens, data analysis