Data science has become an increasingly important paradigm in contemporary education due to the rapid expansion of digital technologies and the growing availability of educational data. This study examines the role of data science in education by exploring its conceptual foundations, applications, benefits, challenges, and implications for teaching, learning, educational management, and policy-making. Drawing on relevant scholarly literature, the review highlights how data science supports evidence-based decision-making, personalized and adaptive learning, improved assessment practices, and efficient educational administration. It also discusses the contribution of data science to educational research and policy development through large-scale data analysis. Despite these advantages, the study identifies key challenges associated with the use of data science in education, including data privacy and security concerns, ethical issues, algorithmic bias, data quality limitations, infrastructural constraints, and inadequate data literacy among educators. The implications of these challenges for educational practice and governance are critically examined. The study concludes that while data science offers significant opportunities for enhancing educational quality, equity, and efficiency, its effective implementation requires robust ethical frameworks, capacity building, and sustained institutional and policy support to ensure responsible and inclusive educational transformation in the digital age.