The rapid development of digital technologies has transformed educational systems worldwide, generating large volumes of educational data through learning management systems, online platforms, and digital assessment tools. Data Science has emerged as a powerful approach for extracting meaningful insights from these data and improving educational effectiveness. This study examines the pedagogical significance of Data Science approaches in modern education, focusing on Learning Analytics, Educational Data Mining, Machine Learning, and Artificial Intelligence technologies. The research employed a systematic literature review and comparative analysis of recent studies related to data-driven educational practices. The findings indicate that Data Science technologies support personalized learning, improve academic performance prediction, facilitate adaptive learning environments, and enhance educational decision-making. Furthermore, these approaches contribute to identifying learning difficulties at early stages and optimizing instructional strategies. However, challenges related to data privacy, algorithmic bias, and technological infrastructure remain significant concerns. The study concludes that Data Science-based educational approaches represent an essential component of future digital pedagogy and educational innovation.