DATA 201 Classification Analytics

Accessible introduction to data scientific classification.  Topics include k-fold cross-validation, data partitioning, partition validation, and protection against overfitting, model building, model evaluation, and making predictions. Topics may include such classification algorithms as neural networks, k-nearest neighbor, and decision trees. 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