Applied Analytics Using SAS Enterprise Miner 5

Duration: 3.0 days CEU: 1.8

This course covers the skills 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).

Learn how to

define a SAS Enterprise Miner 5 project and explore data graphically
apply association and sequence discovery to transaction data
modify data for better analysis results
understand predictive modeling results and regression modeling output
compare and explain complex models
generate and use score code.

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-based 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 necessary.

Course Contents

Introduction

touring SAS Enterprise Miner 5.2
placing SAS Enterprise Miner in the analysis workflow
application examples and case studies
pattern discovery and predictive modeling

Accessing and Assaying Prepared Data

SAS Enterprise Miner system architecture
defining a SAS Enterprise Miner 5.2 project
defining a data source
validating source data

Introduction to Pattern Discovery

clustering and segmenting data
using the Transform Variables node
using the Clustering node for k-means cluster analysis
applying association and sequence discovery
using the Associations node in a consumer banking example
quantifying the associations among items
exploring sequences among items

Introduction to Predictive Modeling with Decision Trees

defining a modeling data source
partitioning data for model development
growing a decision tree with the Desktop Tree Application
running the Decision Tree node
using Decision Tree node options
understanding predictive modeling results data

Introduction to Predictive Modeling with Regressions

comparing linear and logistic regression
using the Regression node
imputing missing values with the Impute node
replacing data values with the Replacement node
performing input selection
understanding regression modeling output
extending regression models with polynomial and interaction terms

Introduction to Predictive Modeling with Neural Networks and Other Modeling Tools

introduction to neural network (multilayer perceptron) models
using the Neural Network node
performing model selection with the AutoNeural node
other SAS Enterprise Miner 5.2 modeling tools

Model Assessment

defining a prior vector
effects of prior vectors on model development
changing model selection criteria
defining a profit matrix
comparing models with the Model Comparison node
introduction to model assessment statistics

Model Implementation

defining a Score data set
scoring a data set with the Score node
using a SAS Code node to export scored data
generating and using score code

Special Topics

using the Variable Selection node
combining models with the Ensemble node
consolidating categorical inputs with the Decision Tree node
explaining complex models with the Decision Tree node

Case Studies

segmenting bank customer transaction histories
association analysis on Web services data
creating a simple risk model from consumer loan data
predicting university enrollment
creating a response model for an insurance product

Software Addressed

This course addresses the following software product(s): SAS Enterprise Miner. This course is appropriate for students who are using SAS Enterprise Miner Release 5.2.

Course Materials

Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.

Registration

To register for this course in the US, call 800-333-7660 or visit support.sas.com/training. This course is also available for on-site training, or you can create a custom course by combining material from several courses. For more details, contact SAS Education in Cary, NC at 919-531-7321 or send e-mail to training@sas.com.