Періодичні видання НАСОА

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Browsing Періодичні видання НАСОА by Subject "big data"

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    Data Analyst and Data Scientist Professions: Demand, Requirements, and Labor Market Prospects
    (Національна академія статистики, обліку та аудиту, 2026) Yashchenko, L. O.
    This article presents a comprehensive study of the current state, requirements, and development prospects of the data analyst and data scientist professions in the context of digital economic transformation and dynamic changes in the labor market. It is demonstrated that the rapid growth of data volumes, the proliferation of analytical platforms, and the active adoption of artificial intelligence and machine learning technologies are driving the increasing strategic importance of data professionals in managerial decision-making processes. The empirical basis of the study is laid by analysis of job vacancies on the Work.ua platform (March 2026), enabling to assess the structure of demand, the level of competition, the requirements to applicants, and the salary characteristics. The findings reveal a structural imbalance between labor demand and supply, shown in the higher number of applicants relative to available vacancies, as well as a gap between salary expectations and actual employer offers. The study systematizes the key competencies of data analysts and data scientists, including technical skills (SQL, Python, BI tools), analytical competencies (statistics, modeling), as well as communicational and managerial skills. It is demonstrated that the modern labor market increasingly demands multidisciplinary professionals capable of working across the full data lifecycle – from data collection to the implementation of business solutions. Special emphasis is placed on the transformation of professional roles, reflected in the blurring of boundaries between business analysts, data analysts, and data scientists, as well as the growing importance of hybrid positions. The analysis revealed a clear trend of transition from descriptive analytics to predictive and prescriptive analytics, significantly enhancing the strategic value of analytical activities. The practical significance of the research lies in potential applications of its findings in improving academic programs, developing professional standards, and setting human capital development strategies in the digital economy context.
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    Organization of Big Data in The Structure of The Digitalization Ecosystem of a Globalized Society
    (Національна академія статистики, обліку та аудиту, 2020) Horobets O.
    The notions of “globalization” and “digitalization” are discussed. The main tendencies of globalization processes, their positive and negative implications for the society are determined. The modules of digitalized eco-systems are described. It is determined that the data sets that currently exist are the engine of innovation and are new alternative sources of statistics for offi cial statistics. An approach to organization of big data is elaborated for demonstrating the data hierarchy. In spite of all the risks globalization opens up new opportunities and eliminate the borders fi rst and foremost for education, R&D, medical services, and manufacturing. Developing in the conditions laid by globalization, countries need to consolidate the effort, because global problems imply global approaches to their solutions.
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    Social Media Data in the Big Data Environment
    (Національна академія статистики, обліку та аудиту, 2021) Osaulenko O.; Horobets O.
    The article contains results of a study of social media data (SMD) which, being distinct from conventional data by their origin, require special methods for collection, processing and analysis. As shown by a literature review, in spite of great many research publications devoted to social media research and big data analysis, the SMD potential as a big data component still remains inadequately explored. Two approaches to research and analysis of SMD were highlighted in course of the study, in which SMD are addressed as an object of Internet statistics and an object of big data. When SMD are explored as an object of Internet statistics, collection of anonymized data is performed using the services that have network protocols for collection and analysis of data on social media customers using statistical methods. When SMD are explored as an object of big data, the collection is performed mostly by artifi cial intellect, whereas the storage and processing is operated by databases designed for large scopes of data and software with statistical data processing applications. The social media most popular with users in 2020 were identifi ed in the study. Statistical indicators for assessment of users’ feedback, available now for statistical assessments of social media communities, are given. The study revealed several problems which solutions would require, apart from a multifaceted and complex approach to collection and processing, highly competent teams of specialists in various subject fi elds, including experts in computations, experts in machine learning and statisticians.