Foundations of Data Science

Catalog description

The course combines theoretical foundations with hands-on computational practice. Topics include introductory linear algebra, probability, statistics, optimization, information theory, and mathematical modeling, with an emphasis on how these ideas support modern data analysis and machine learning. Students will implement algorithms and analyze real-world datasets in Python drawn from diverse domains such as images, video, language, genomics, music, and art. Prerequisites: Calculus I and Data Structures; Discrete Mathematics may be taken concurrently.