The qualification thesis, titled "Applying Data Mining Techniques for Estimating the Digital Maturity Level of Small and Medium Enterprises," is a Master's degree project by Olha Hlado from Ternopil Ivan Puluj National Technical University. The thesis focuses on clustering data from a survey of entrepreneurs in Ternopil region regarding the digitalization of business processes in their companies and analyzing the results. The first section of the thesis covers the general concepts of data analysis and business analytics, discussing various types of tasks and methods used to solve them. The second section delves into the detailed review of the clustering problem, its methods, approaches, distance measures, and quality metrics. The third section describes the development of specialized software for performing clustering tasks, conducting data clustering using multiple methods, and visualizing and analyzing the results. Additionally, the thesis briefly examines the digital transformation index (HIT) and highlights common features in the issue of digitalization of small and medium enterprises. The research aims to identify new valuable ideas about understanding the current state of digital technology usage in small and medium enterprises through various data clustering methods. The thesis is structured into four chapters, conclusions, a list of references, and six appendices. It consists of 124 pages, including 85 pages of main text, 20 figures, and 7 tables. The thesis is supervised by Professor I.V. Strutinskaia and approved by the rector's order dated October 28, 2021, with the reference number 4/7-909. The deadline for submitting the completed work was June 26, 2022.The qualification thesis, titled "Applying Data Mining Techniques for Estimating the Digital Maturity Level of Small and Medium Enterprises," is a Master's degree project by Olha Hlado from Ternopil Ivan Puluj National Technical University. The thesis focuses on clustering data from a survey of entrepreneurs in Ternopil region regarding the digitalization of business processes in their companies and analyzing the results. The first section of the thesis covers the general concepts of data analysis and business analytics, discussing various types of tasks and methods used to solve them. The second section delves into the detailed review of the clustering problem, its methods, approaches, distance measures, and quality metrics. The third section describes the development of specialized software for performing clustering tasks, conducting data clustering using multiple methods, and visualizing and analyzing the results. Additionally, the thesis briefly examines the digital transformation index (HIT) and highlights common features in the issue of digitalization of small and medium enterprises. The research aims to identify new valuable ideas about understanding the current state of digital technology usage in small and medium enterprises through various data clustering methods. The thesis is structured into four chapters, conclusions, a list of references, and six appendices. It consists of 124 pages, including 85 pages of main text, 20 figures, and 7 tables. The thesis is supervised by Professor I.V. Strutinskaia and approved by the rector's order dated October 28, 2021, with the reference number 4/7-909. The deadline for submitting the completed work was June 26, 2022.