Applied Analytics Using SAS Enterprise Miner

  • Data analysts
  • Qualitative experts
  • Individuals who want an introduction to SAS Enterprise Miner

Prerequisite

  • Familiarity with Microsoft Windows and Windows software
  • An introductory-level familiarity with basic statistics and regression modeling
  • Previous SAS software experience is helpful but not required

Expected Duration
3 day

Description

In this course, you will learn how 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).

Objective

1. Introduction

  • Introduction to SAS Enterprise Miner

2. Accessing and Assaying Prepared Data

  • Creating a SAS Enterprise Miner project, library, and diagram
  • Defining a data source
  • Exploring a data source

3. Introduction to Predictive Modeling: Predictive Modeling Fundamentals and Decision Trees

  • Cultivating decision trees
  • Optimizing the complexity of decision trees
  • Additional diagnostic tools (self-study)
  • Autonomous tree growth options (self-study)

4. Introduction to Predictive Modeling: Regressions

  • Selecting regression inputs
  • Optimizing regression complexity
  • Interpreting regression models
  • Transforming inputs
  • Categorical inputs
  • Polynomial regressions (self-study)

5. Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools

  • Input selection
  • Stopped training
  • Other modeling tools (self-study)

6. Model Assessment

  • Model fit statistics
  • Statistical graphics
  • Adjusting for separate sampling
  • Profit matrices

7. Model Implementation

  • Internally scored data sets
  • Score code modules

8. Introduction to Pattern Discovery

  • Cluster analysis
  • Market basket analysis (self-study)

9. Special Topics

  • Ensemble models
  • Variable selection
  • Categorical input consolidation
  • Surrogate models
  • SAS Rapid Predictive Modeler

10. Case Studies

  • Banking segmentation case study
  • Website usage associations case study
  • Credit risk case study
  • Enrollment management case study

SUBSCRIPTION COST


$2,400.00

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