Students are introduced to the central ideas used in data science. Topics include supervised and unsupervised algorithms in regression; classification; and clustering problems; probabilistic results such as bias-variance trade-off and sampling variability; and ensemble methods. Concepts are explored and interpreted using a common statistical programming language such as Python or R. (Spring; odd years)
Prerequisites
Semester Offered
Spring; odd years