DATA 202 Estimation and Clustering Analytics

Accessible introduction to data scientific estimation and clustering.  Topics include estimation algorithms such as linear regression and clustering algorithms such as k-means clustering, and making predictions.  Topics may include multiple regression modeling, model building, hierarchical clustering, and evaluating cluster goodness.  Deeper familiarity with an open-source data science platform, such as R.

Credits

4

Prerequisite

DATA 101 and STAT 201, or permission of department chair.

General Education

Offered

  • Fall and Spring