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STAT 615. Data Mining for Analytics

Credits: 3
Department: Statistics
Description: Data mining principles and applications. Predictive modeling techniques for large data sets include classification and regression trees, logistic regression, neural networks, random forests and boosted trees. Handle missing values and outliers. Compare models and deploy best model to predict new data. Extensive hands-on use of data mining software.
Prerequisites: STAT 242 or equivalent
Semester Offered: Spring
Grading Method: ABCDF

Student Learning Outcomes

1. Students will be able to explore large data sets graphically to better understand the data.
2. Students will be able to describe data mining principles.
3. Students will be able to explain the history of data mining and today’s important applications.
4. Students will be able to choose and apply appropriate predictive modeling techniques.
5. Students will be able to use data mining software.

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