Applied Analytics Using SAS Enterprise Miner

**Register* for Upcoming Training**

May 14 - 16

October 22 - 24

*Please Note: Completing the registration form will result in the generation of an invoice. Payment is expected within 30 days of course completion. Cancellation less than two weeks prior to class start date will result in a cancellation fee equal to 50% of the registration cost. No Exceptions.

Course Overview

This course is appropriate for SAS Enterprise Miner from release 5.3 up to 14.2. The 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 can help prepare you for the following certification exam(s): Predictive Modeling Using SAS Enterprise Miner.

Learn how to

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

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

Prerequisites

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.

Pricing

Applied Analytics Using SAS Enterprise Miner: $2,400

Course Outline

Note: This course will last all day for the length of the course. Most classes will start at 9:00AM and end at 5:00PM.

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

Course Logistics

Location: FedEx Institute of Technology, 365 Fogelman Drive
Duration: 9:00 AM - 5:00 PM CST

We ask that attendees bring their own device in order to follow along with the class presentations. The temperature in our training room tends to be cooler, we advise you to bring a light jacket or sweater. Parking passes are provided for the garage across to the FedEx Institute of Technology (located on Fogelman Drive).

If you have any additional questions please let us know at fedex@memphis.edu.

Travel Information

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