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Applied Analytics Using SAS Enterprise Miner

GK# 2457

Course Overview

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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).

Schedule

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  • Delivery Format:
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What You'll Learn

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  • Define a SAS Enterprise Miner project and explore data graphically
  • Modify data for better analysis results
  • Build 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

Outline

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Classroom Live Outline

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

Labs

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Classroom Live Labs

Exercises or hands-on workshops are included with most SAS courses.

Prerequisites

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  • 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

Who Should Attend

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

Follow-On Courses

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There are no follow-ons for this course.

Course Delivery

This course is available in the following formats:

Classroom Live

Receive face-to-face instruction at one of our training center locations.

Duration: 3 day

Virtual Classroom Live

Experience expert-led online training from the convenience of your home, office or anywhere with an internet connection.

Duration: 6 day

Request this course in a different delivery format.
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