COURSE CURRICULUM

The Master of Science in Data Science at Rochester Institute of Technology follows a structured 30-credit curriculum in its on-campus option, typically completed over two years. The program begins in the first-year fall semester with foundational courses such as data science and analytics, applied statistics, and software construction. These courses build a strong base in data handling, statistical reasoning, and programming, ensuring students develop essential technical and analytical skills required in the field.In the spring semester of the first year, the curriculum becomes more applied and interdisciplinary. Students take courses like Applied Data Science I, software engineering for data science, and database design and implementation, along with an elective. This phase focuses on practical skills such as working with real datasets, designing scalable systems, and integrating data science methods into software solutions. It prepares students to handle real-world data challenges and develop efficient data-driven applications.During the second year, students deepen their expertise through advanced coursework and electives. In the fall semester, they complete Applied Data Science II along with two additional electives, allowing them to specialize in areas aligned with their career interests, such as machine learning, big data, or data visualization. The elective flexibility enables students to tailor their learning experience and gain deeper knowledge in specific domains.Overall, the curriculum provides a balanced combination of statistics, programming, software engineering, and applied data science. It emphasizes both theoretical understanding and hands-on experience, preparing graduates for roles in data analytics, machine learning, and data-driven decision-making across industries.