Educational Data Analytics Foundations

Educational institutions generate massive amounts of data daily, but turning those numbers into actionable insights requires a specific skill set. By understanding how to interpret this information, educators and administrators can significantly improve student success and institutional effectiveness. This course provides a foundational introduction to educational data analytics. You will learn how to transition from simply collecting raw metrics to implementing data-driven teaching and learning strategies. We cover essential terminology, core analysis techniques, and modern approaches to identifying student needs while maintaining strict ethical standards and data privacy. What you'll learn: - Understand the core terminology and concepts of educational data analytics. - Analyze student performance metrics to optimize teaching and learning strategies. - Apply modern data privacy principles and ethical guidelines to handle sensitive student information. - Identify patterns and early warning signs to support at-risk students proactively. - Translate raw educational data into actionable insights for institutional decision-making. - Explore fundamental concepts of predictive modeling used in modern educational technology. The course begins by establishing key definitions and foundational concepts before moving into practical analysis workflows. You will progress through reading materials and written exercises that build your confidence in interpreting educational metrics and designing data-informed interventions. Designed entirely for beginners, this text-based course requires no prior experience in data science, programming, or advanced statistics. It is ideal for educators, administrators, and anyone interested in the intersection of data and learning. Start reading today to transform how you use data to support student success and educational excellence.