USE OF INTELLIGENT METHODS IN STATISTICAL MODELING IN THE TRAINING OF BUSINESS ANALYSTS

Introduction. Today, the active development of intelligent data analysis methods is driven by the need to analyze and process large amounts of information stored in modern databases. Most companies accumulate huge amounts of data in the course of their operations and want to get useful information from it. Data mining technology is mainly needed by managers, analysts, specialists, consultants, and other professionals who make important decisions. In the training of business intelligence specialists, the use of intelligent statistical modeling methods is an important part of the modern process. The combination of classical statistical methods with artificial intelligence and machine learning techniques allows you to better understand, predict, and analyze large amounts of data. Technical skills help you understand data, systems, and complex business processes, analyze problems to be solved, communicate with technical stakeholders, and develop systems thinking. In addition, they can develop a basic understanding of systems analysis and knowledge of terminology. Without technical skills, business analysts cannot effectively define architecture, integration, and database requirements, analyze and evaluate information system options, or speak the same language as developers and architects. The role and place of intelligent methods in statistical modeling needs to be clarified in the training of business analysts. Purpose. The aim of the study is to reveal conceptual approaches to the use of intelligent methods in statistical modeling in the training of business analysts. Materials and methods. A variety of research materials, including scientific papers, studies, and publications, collected from the relevant databases and professional journals, aimed at using intellectual methods in statistical modeling in the development of business analytics specialists, have been used to write the research paper. Various methods were used in writing the articles, including abstract and logical analysis, generalization, analysis and synthesis, in order to ensure a complete reflection of the material. Results. The article explores the importance of using intelligent methods in statistical modeling, since modern companies face a wide range of challenges and opportunities, and analytics is becoming an important tool for understanding and optimizing processes. It is noted that the digital transformation of the economy has become a widespread phenomenon in the modern world and, as noted, is revolutionizing scientific approaches, methods and research infrastructure. Intelligent information technologies, in particular artificial intelligence, data analytics, virtual reality, machine learning, Bayesian methods, evolutionary algorithms, etc. play an important role in this process, ensuring a change in approaches to the development of intelligent systems, improving quality and creating new opportunities.