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This course covers the skills that are required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models). This course is appropriate for SAS Enterprise Miner 5.3 up to 15.1.

Before attending this course, you should be acquainted with Microsoft Windows and Windows software. In addition, you should have at least an introductory-level familiarity with basic statistics and regression modeling. Previous SAS software experience is helpful but not required.

This course addresses SAS Enterprise Miner software.

Who should attend
Data analysts, qualitative experts, and others who want an introduction to SAS Enterprise Miner

Define a SAS Enterprise Miner project and explore data graphically.
Modify data for better analysis results.
Build and understand predictive models such as decision trees and regression models.
Compare and explain complex models.
Generate and use score code.
Apply association and sequence discovery to transaction data.

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.

Upcoming Trainings

Join our public courses in our New Zealand facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

Classroom / Virtual Classroom

03 August 2024