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.
Prerequisites
Semester Offered
Odd Years