A B S T R A C T

The purpose of the research - is to examine and evaluate the development dynamics of the global big data market, the nature and capabilities of traditional and generative artificial intelligence, and the most important and widespread artificial intelligence methods and tools used in data analysis.

The methodology of the research - key promising areas for statistical information analysis currently include improving existing statistical methodology for the complete analysis of big data, as well as the widespread use of artificial intelligence methods and tools.

The practical importance of the research - one of the key areas for improving statistical information analysis methodology in the digital age is the development of a methodological approach to analyzing big data based on machine learning. Machine learning methods offer greater flexibility than traditional statistical methods, which can have a positive impact on the process of statistical information analysis in practice.

The results of the research - the study examines a methodology for analyzing statistical information based on machine learning methods, its main advantages, and the specific stages of constructing a machine learning algorithm. This may have significant practical implications for improving the quality of data analysis using artificial intelligence, as well as the analytical capabilities of information support for decision-making at all levels.

The originality and scientific novelty of the research - to ensure that statistics are adequate to global challenges, serving as an important tool for assessing the progress of society and the effectiveness of public policy, the study identifies the potential and trends for the application of artificial intelligence technologies in data analysis in the future, as well as the role of international projects. This study can be considered an original research work.

Keywords: statistics, artificial intelligence, large data, data analysis, deep analysis, machine learning, neural networks.

http://doi.org/10.59610/bbu4.2025.4.1