THE R PROGRAMMING LANGUAGE IN STEM-BASED TEACHING OF STATISTICS AND DATA ANALYSIS

Abstract

The relevance of implementing the STEM approach in teaching statistics and data analysis within the modern education system is considered. It is shown that the traditional methodology of teaching statistics is mainly focused on the formal apparatus and classical models, which complicates students’ understanding of the applied value of probabilistic thinking in real-life conditions. Emphasis is placed on the importance of integrating mathematical theory with practical tools, which is achieved through the application of the STEM approach that combines scientific knowledge, technologies, engineering logic, and mathematical apparatus. From this perspective, the R programming language occupies a special place, as it provides a powerful toolkit for statistical analysis, data visualization, model building, and hypothesis testing. A review of scientific sources confirms the relevance of STEM education as a factor in the development of critical, algorithmic, and engineering thinking. The possibilities of using R in the educational process and its compliance with the main components of STEM (Science, Technology, Engineering, Mathematics) are demonstrated. Examples of students’ practical activities in the R environment are presented, ensuring an interdisciplinary approach to learning and contributing to the development of analytical competencies. A comparative analysis of R, Python, and Excel/Google Sheets according to key criteria of effectiveness in the context of STEM education is carried out, which highlighted the unique advantages of R in teaching statistics and developing applied skills. It is concluded that, despite the popularity of other programming languages in data analytics, R has significant potential in the educational sphere due to its statistical roots, developed package ecosystem, and wide opportunities for interactive data work. The integration of such approaches into the learning process allows not only mastering the basic tools of data analysis but also ensuring students’ readiness to create and implement artificial intelligence systems in various fields. The use of R in education helps overcome the gap between theoretical knowledge and practical needs, increases students’ motivation, and ensures the training of competitive specialists in data analysis.