COMP 467 Interdisciplinary Data Practicum

Many disciplines rely on large data collections to make informed disciplinary conclusions. At the same time, disciplinary knowledge is crucial to extracting the important information from the data. This project-based course will engage teams of students, at the junior level or above, from computer science and other disciplines to analyze real world data using techniques of data analysis, visualization, and interpretation. Topics will include data cleaning, feature selection, and techniques from statistical inference and machine learning, combined with domain-specific knowledge. The course will be team-taught by faculty in Computer Science and the discipline of the application. May be repeated once for credit when disciplinary combinations differ.