Applied Analytics Using SAS(R) Enterprise Miner(TM)

Target group

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.