## Applied Analytics Using SAS Enterprise MinerHá uma nova versão para esse curso. Por favor, veja Applied Analytics Using SAS Enterprise Miner. This training is appropriate for SAS Enterprise Miner 14.1. 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). Aprenda como
- 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.
## Quem poderá participarData 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 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. Introduction- 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- 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- 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 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
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