Sapar A. Issayev
Methods of teaching data analysis and machine learning disciplines
Abstract.
In the context of societal digital transformation, disciplines related to data analysis and machine learning are of particular importance in the higher education system. Their implementation ensures the development of not only professional skills in working with big data, but also the analytical and critical thinking necessary for effective operation in the digital economy. on the other hand, it fosters their readiness to integrate modern digital technologies into school and college education. The article examines the theoretical and methodological foundations of teaching data analysis and machine learning disciplines, using the example of the Kazakh National Women’s Pedagogical University. Particular attention is paid to the problems and challenges of implementing these disciplines, including the heterogeneity of student training levels, limited technical resources, and a lack of adapted teaching materials. Based on the analysis, effective methodological solutions are identified: modular structure of courses, use of open data and cloud services, project-based learning, integration of BI systems, and programming tools. The practical experience of implementing courses is presented, and the prospects for developing the direction are outlined, including interdisciplinary integration, the use of artificial intelligence technologies, and international cooperation. The results of the study demonstrate that the methodology of teaching data analysis and machine learning in a pedagogical university is a strategic resource for modernizing education and an essential condition for training the next generation of teachers.