In this course, students will be exposed to scientific methods and processes to extract knowledge and insights from structured and unstructured data. This course will leverage advanced statistics, data analysis, machine learning and related data methodologies to analyze, understand, model, and gain novel knowledge from data. Students will be introduced to clinical epidemiology, predictive analytics, comparative effectiveness and health services research, clinical predication rules, and patient centered outcomes research. Students will learn to apply healthcare analytics including learning how to apply data in quality and performance improvement and innovation projects. Introduction to research informatics tools such as REDCap, i2b2, and TriNetXwill be taught. OMAP will be introduced.
Students will learn database design and modeling using a hands on experience. Conceptual model: the logical structure of the entire database. The course will address conceptual schemas, database logical design, entity relationship diagram (ERD), external and internal models, normalization, and data independence (logical and physical).