Data Mining
General
Prefix
STAT
Course Number
415
Course Level
Undergraduate
Department/Unit(s)
College/School
College of Science and Engineering
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. Hands-on use of data mining software.
Prerequisites
Credits
Min
3
Max
3
Repeatable
No
Goals and Diversity
MN Goal Course
No
Cultural Diversity
No
Learning Outcomes
Outcome
Explore large data sets graphically to better understand the data.
Outcome
Describe data mining principles.
Outcome
Explain the history of data mining and today¿s important applications.
Outcome
Choose and apply appropriate predictive modeling techniques.
Outcome
Use data mining software.
Course Outline
Course Outline
Dependencies
Programs
STAT415
is a
completion requirement
for: