COURSE CURRICULUM

The Master of Science in Applied Statistics at Rochester Institute of Technology follows a flexible 30-credit curriculum that combines core statistical theory with customizable electives to match students’ career goals. In the first year, students build a strong foundation in statistical methods through courses such as Foundations of Statistics and Applied Linear Models, covering both regression and ANOVA, which are essential for analyzing real-world data. Alongside these core courses, students begin selecting electives to explore specialized areas like data mining, machine learning, or applied analytics. In the second year, the program becomes more personalized, with multiple electives allowing students to deepen their expertise in specific domains such as healthcare analytics, industrial statistics, or business intelligence. The program culminates in a capstone thesis or project, where students apply their knowledge to solve a practical, real-world problem or conduct independent research. Overall, the curriculum emphasizes both theoretical understanding and practical application, preparing graduates with strong analytical, computational, and problem-solving skills needed for data-driven roles across industries.