This 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 is appropriate for SAS Enterprise Miner 5.3 up to the current release.
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.Who Should Attend
Data analysts, qualitative experts, and others who want an introduction to SAS Enterprise Miner
Prerequisites
Before attending this course, you should be acquainted with Microsoft Windows and Windows 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.
SAS Products Covered
SAS Enterprise Miner
Course Outline
Introduction
Introduction to SAS Enterprise Miner.Accessing and Assaying Prepared DataCreating 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 TreesIntroduction. 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: RegressionsSelecting 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 ToolsInput selection. Stopped training. Other modeling tools (self-study).Model AssessmentModel fit statistics. Statistical graphics. Adjusting for separate sampling. Profit matrices.Model ImplementationInternally scored data sets. Score code modules.Introduction to Pattern DiscoveryCluster analysis. Market basket analysis (self-study).Special TopicsEnsemble models. Variable selection. Categorical input consolidation. Surrogate models. SAS Rapid Predictive Modeler.Case StudiesBanking segmentation case study. Website usage associations case study. Credit risk case study. Enrollment management case study.
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