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Applied Analytics using SAS Enterprise Miner (AAEM71)

Course duration: 3 days


This training is appropriate for SAS Enterprise Miner 5.3, 6.1, 6.2, and 7.1.

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 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
  • use other modeling tools such as rule induction, gradient boosting, and support vector machines.

Who should attend

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


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 required.

Course Outline


  • 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 with Decision Trees
  • 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 with Regressions
  • selecting regression inputs
  • optimizing regression complexity
  • interpreting regression models
  • transforming inputs
  • categorical inputs
  • polynomial regressions (self-study)
Introduction to Predictive Modeling with Neural Networks and Other Modeling Tools
  • introduction to neural network models
  • 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 set
  • 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
  • segmenting bank customer transaction histories
  • association analysis of Web services data
  • creating a simple credit risk model from consumer loan data
  • predicting university enrollment management

Course Materials

Classroom: Students attend classroom courses in one of our public training centers. You receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.

Live Web: Students attend Live Web classes using a Web browser and a telephone and interact with their instructor and fellow classmates in real time. Each student receives an e-mail with instructions on how to join the class three business days before the class begins. The instructions e-mail includes a link to download the course materials. Students need to download and print the course materials prior to class.

e-Course: Students have unlimited 24/7 access to the product for the specified license period. The course requires only a standard Web browser and a free Flash plug-in. The course includes data that can be downloaded, printable lesson summaries, a Quick Reference Guide, practices and solutions, step-by-step demonstrations, and a Certificate of Completion.

System Requirements

This course addresses SAS Enterprise Miner software.

For Live Web,

  • review and meet the general system requirements.
  • complete the course exercises through our virtual lab. The virtual lab allows you to access the software used in class over the Internet, so that you do not need this software on your local machine.
  • run this test to connect to a virtual lab session.
For e-Courses,
  • use Internet Explorer 6.0 or later or Firefox 1.5 or later, with JavaScript and cookies enabled
  • have the latest version of Macromedia Flash Player installed
  • have base SAS software visible on the same machine that you are taking the training on so that you can practice your new skills in your own SAS environment.

Location Start Day Duration Fee
Dubai 3 days 1500.0 US |
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