DATA 202 Estimation and Clustering Analytics
Accessible introduction to data scientific estimation and clustering. Topics include estimation algorithms and the k-means clustering algorithm. Basic introduction to regression modeling, model building, and evaluating goodness of fit. Deeper familiarity with a popular data science software platform.
Credits
3
Prerequisite
DATA 101 and
STAT 201 (C- or better), or permission of department chair.