Target group
Data analysts, qualitative experts, and others who want an introduction to SAS Enterprise Miner
Course structure
Introduction
- Introduction to SAS Enterprise Miner.
Accessing and Assaying Prepared Data
- Creating a SAS Enterprise Miner project, library, and diagram.
- Defining a data source.
- Exploring a data source.
Introduction to Predictive Modeling: Predictive Modeling Fundamentals and Decision Trees
- Introduction.
- Cultivating decision trees.
- Optimizing the complexity of decision trees.
- Understanding additional diagnostic tools (self-study).
- Autonomous tree growth options (self-study).
Introduction to Predictive Modeling: Regressions
- Selecting regression inputs.
- Optimizing regression complexity.
- Interpreting regression models.
- Transforming inputs.
- Categorical inputs.
- Polynomial regressions (self-study).
Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools
- Input selection.
- Stopped training.
- Other modeling tools (self-study).
Model Assessment
- Model fit statistics.
- Statistical graphics.
- Adjusting for separate sampling.
- Profit matrices.
Model Implementation
- Internally scored data sets.
- Score code modules.
Introduction to Pattern Discovery
- Cluster analysis.
- Market basket analysis (self-study).
Special Topics
- Ensemble models.
- Variable selection.
- Categorical input consolidation.
- Surrogate models.
- SAS Rapid Predictive Modeler.
Case Studies
- Banking segmentation case study.
- Website usage associations case study.
- Credit risk case study.
- Enrollment management case study.
