Applied Analytics Using SAS Enterprise Miner 5 (AAEM)

Duration

3 days

Description

This course provides extensive hands-on experience with SAS Enterprise Miner. It covers the basic skills required to assemble analyses using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modelling (decision tree, regression, and neural network models). The course includes completed case studies from the fields of database marketing, financial services, web analytics, and higher education.

Prerequisite Skills

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 modelling. Previous SAS software experience is helpful but not necessary.

Course Topics

Introduction

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

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 Modelling with Decision Trees

• defining a modelling 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 modelling results data

Introduction to Predictive Modelling 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 modelling output
• extending regression models with polynomial and interaction terms

Introduction to Predictive Modelling with Neural Networks and Other Modelling 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 modelling 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 enrolment
• creating a response model for an insurance product

Booking

Please contact the Education Team at SAS for the latest information on all SAS courses or to put your name on our specialised course waiting list.