Analysis of the Content of Academic Disciplines Related to the Study of Big Data

Abstract

Abstract: The purpose of this study is to conduct an analysis of the content and structure of educational disciplines and courses devoted to Big Data technologies, which are implemented in domestic and foreign universities, as well as in non-academic learning environments. Particular attention is paid to large-scale open online courses (MOOC platforms), professional IT schools, and certification programs developed by leading technology companies. The research seeks to identify similarities and differences in the educational content of these approaches and to determine how they contribute to the formation of competencies necessary for modern specialists in the digital economy. To achieve this aim, a comparative analysis of curricula, syllabi, and instructional modules was carried out. The review covered a wide range of thematic lines, including theoretical foundations of Big Data, data analysis algorithms and statistical models, programming languages commonly applied in data processing (Python, SQL, R, Scala), as well as cloud platforms and distributed data processing tools (Hadoop, Spark, Kafka). Special attention was given to modules on machine learning, data visualization, and interpretation of results, as well as project-based learning activities and real-world case studies. This methodological approach allowed us to trace the balance between theoretical knowledge and practical skill acquisition across different educational formats. The findings demonstrate that academic programs primarily emphasize the acquisition of fundamental knowledge, interdisciplinary integration, and research-oriented preparation, enabling students to develop analytical thinking and scientific inquiry skills. In contrast, non-academic programs are designed to provide learners with rapid access to practical skills, strong professional mobility, and readiness for industry-recognized certification exams. Despite these distinctions, both academic and non-academic models reveal the presence of a common core of competencies. These shared components include knowledge of data storage and processing, basic statistical analysis, the ability to use modern programming tools, and skills in interpreting and communicating analytical results to diverse audiences. The study highlights that the academic and non-academic approaches to Big Data education are not mutually exclusive but rather complementary. The integration of elements from both systems makes it possible to combine the strengths of theory-driven higher education with the flexibility and applied orientation of professional training. Such a blended approach is particularly relevant under the conditions of the rapidly growing digital economy, which requires professionals capable of continuous learning and adaptation to new technological environments. The article concludes that the effective development of Big Data competencies demands hybrid curricula that incorporate fundamental, research-based preparation together with hands-on, practice-oriented training. Keywords: Big Data, education, Data Science, data analytics, cloud technologies, academic and non-academic programmes, competences.