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Department of Digital Economy and System Analysis
Department of Digital Economy and System Analysis
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Department of Digital Economy and System Analysis
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Scientific Work

of Digital Economy and System Analysis

Official account of the Department of Digital Economy and System Analysis: Google Scholar : Google Scholar.

TRENDS IN THE DEVELOPMENT OF SCIENTIFIC DIRECTIONS OF THE DEPARTMENT OF DIGITAL ECONOMY AND SYSTEM ANALYSIS 

 The Department of Digital Economy and System Analysis carries out research in two main areas:

  • Data Science;
  • mathematical and computer modeling of digital economy processes. 

Data Science

The amount of information in the world has reached enormous proportions. This is eloquently evidenced by the fact that 90% of the data acquired by mankind during its existence was received in the last two years, and by the end of 2020 this amount of information will double and reach an unprecedented value of 40,000 exabytes.

Data Science is an interdisciplinary field of scientific methods, processes and systems related to the extraction of knowledge from data in various forms (both structured and unstructured). 

It combines fields such as mathematics, statistics, computer science, machine learning etc. A Data Science professional is distinguished by extensive cross-disciplinary knowledge and super-ability to analyze. 

Glassdoor, the most authoritative website with the information about the work, which annually publishes the 50 most relevant professions in the United States, prefers the data analysis specialists for four years in a row.

According to the latest World Economic Forum report in 2022, 85% of companies in the world will need, first of all, data analysis specialists. Among the professional areas that are actively included in all areas of activity, it is data analytics, work with big data, artificial intelligence and machine learning. 

In 2019, the World Economic Forum report on the importance of Data Science in the global economy was published. Real data on the need for Data Science Skills in all areas of human activity were given. 

Main directions of the Department’s research in the field of Data Science: 

  • comprehensive business analysis of complex systems of different nature based on Data Science methodology; 
  • construction and analysis of expert systems and artificial intelligence systems; 
  • development and systematic analysis of machine learning methods and algorithms for business process optimization; 
  • identification of basic data that affect the development of physical, economic, and social processes; the extraction of stochastic and uncertain factors and the study of relationships between them; 
  • solving problems of intellectual analysis of big data in various fields of science, technology, finance, socio-economic and political spheres and the national economy as a whole; 
  • computer implementation of mathematical models of real processes and systems; 
  • use of data analysis software (Power BI, Tableau, RapidMiner), universal and specialized programming languages (C #, Java, Python, R, SQL), simulation languages for system research. 

Main scientific publications of the Department’s academic staff in the field of Data Science: 

  1. Roskladka A. The data science tools for research of emigration processes in Ukraine / A. Roskladka, N. Roskladka, G. Kharlamova, A. Karpuk, A. Stavytskyy // Problems and Perspectives in Management - Volume 18, issue #1, 2020, p. 70-81 (Scopus).
  2. Roskladka A. Data analysis and forecasting of tourism development in Ukraine / A.Roskladka, N. Roskladka, O. Dluhopolskyi, G. Kharlamova, M. Kiziloglu // Innovative Marketing. - Volume 14, 2018, issue #4, pp. 19-33 (Scopus).
  3. Roskladka A. Data Scientist: a glance into the future / A. Roskladka, O. Ivanova, V. Kulazhenko // Зовнішня торгівля: економіка, фінанси, право. – 2019. – № 3. – C. 109- 120.
  4. Роскладка А. А Кластерний аналіз клієнтської бази даних підприємств сфери послуг / А.А. Роскладка,Н.О. Роскладка, О. О. Дзигман // Агросвіт, 2019. – №16. – С. 8-17.
  5. Роскладка А. А. Моделювання процесу консолідації даних агропромислового підприємства / А.А. Роскладка // Вісник Одеського національного університету. – 2015. – № 20 (2/1). – С. 191-194.
  6. Roskladka A. Practical implementation of the methodology of forming a system for monitoring the process of information support / A. Roskladka, R. Baglai, V. Lazurenko, M. Zaichenko // Big Data Processing: methods, models and information technologies: monograph. – Shioda GmbH, Steyr, Austria, 2019. – P. 161-188.
  7. Roskladka A. Formation of the monitoring system for non-production enterprises / A. Roskladka, N. Roskladka, V.Hamalii, N. Geseleva // Big Data processing: methods, models and information technologies. Shioda GmbH, Steyer, Austria, 2019. P. 188-215.
  8. Proniuk G., Geseleva N., Kyrychenko I., Tereshchenko G. Spatial Interpretation of the Notion of Relation and Its Application in the System of Artificial Intelligence [Електроннийресурс] / G. Proniuk, N. Geseleva, I. Kyrychenko, G. Tereshchenko // CEUR Workshop Proceedings of the 3rd International Conference on Computational Linguistics and Intelligent Systems (COLINS2019). Volume I: Main Conference, Kharkiv, Ukraine, April 18-19, 2019. – Режимдоступудоресурсу: http://ceur-ws.org/Vol-2362/paper24.pdf
  9. Кулаженко В. В. Нейромережеве моделювання процесу економічно безпеки підприємства / В. В. Кулаженко // Колективна наукова монографія «Проблеми та перспективи економічної кібернетики». – К.: ВД ТЗОВ «AgrarMediaGroup», 2013. – С. 226–231.
  10. Kulazhenko V. E-trade market analusis using data clustering methods. Big Data processing: methods, models and information technologies: monograph/Pursky O., Moroz I., Ivanova I., Kulazhenko V. – edited by Oleg I. Pursky. – Shioda GmbH, Steyr, Austria, 2019. – 90-161 pp.

Mathematical and computer modeling of digital economy processes 

Digital economy is a scientific field applying modern digital technologies to management of economic systems. In this area,  modeling, research and organization of management processes in economic systems are conducted with the use of modern information technology.

The digital economy is one of the most relevant and prestigious areas in higher education today. Growing computerization and informatization of all spheres of economy and public life, improvement of means of information modeling and decision support in any economic and social structure provide urgency and obvious prospects of specialists in digital economy, from building the Internet of Things and developing blockchain technologies to modeling of global world macroeconomic processes.