Applied Analytics Using SAS Enterprise Miner 5

Duration: 3.0 days Course fee: $2,175 EPTO units: 4.2 CEUs: 1.8

This outline is provisional and subject to change.

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
modify data for better analysis results
build and understand predictive models like decision trees and regression models
compare and explain complex models
generate and use score code
apply association and sequence discovery to transaction data
use other modeling tools like rule induction, gradiant boosting, and support vector machines.

Who should attend

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

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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 5.3
placing SAS Enterprise Miner in the analysis workflow
application examples and case studies
pattern discovery and predictive modeling

Accessing and Assaying Prepared Data

defining a SAS Enterprise Miner 5.3 project
defining a data source
validating source data

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 (self-study)
other SAS Enterprise Miner 5.3 modeling tools (self-study)

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

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 (self-study)
using the Associations node in a consumer banking example (self-study)
quantifying the associations among items (self-study)
exploring sequences among items (self-study)

Special Topics (Self-Study)

reducing irrelevancy
reducing redundancy
combining models with the Ensemble node
consolidating categorical inputs with the Decision Tree node
explaining complex models with the Decision Tree node

Case Studies (Self-Study)

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

This course addresses SAS Enterprise Miner. This course is appropriate for students who are using SAS Enterprise Miner Release 5.3.

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.

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Are there additional topics you'd like for this course to address? Would you like for this course to be offered at another training facility? Let us know by adding to our Interest List.

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