Introduction to the analysis of data using a data scientific methodology.  Topics include data preparation, missing data, data cleaning, exploratory data analysis, statistical estimation and prediction, cross-validation, model evaluation techniques, misclassification costs, cost-benefit analysis, classification and regression trees and report writing. 

Prerequisites: B or better in a first semester statistics course, such as STAT 104 or STAT 200 or STAT 215 or permission of department chair.

4 Credits

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