Applied Analytics Using SAS Enterprise Miner

This course is appropriate for SAS Enterprise Miner from release 5.3 up to 14.2.

Duration 3 Days
Certificate SAS Global
Language English

Fees 2400

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About Program

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.

Format of Training

Taught by certified instructors at high-tech facilities across the country

All the benefits of the classroom without the travel

  • Join the classroom right from your desktop
  • Led by an expert instructor who can virtually look over your shoulder
  • Ask questions and get answers in real-time
  • Access the latest software via a virtual lab
  • Receive 20 business days' access to a recording of your course
  • Discuss, share, exchange ideas with participants from different countries

Training Features

  • 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.

Course Curriculum


  • 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 Fees





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