Applied Analytics Using SAS(R) Enterprise Miner(TM)
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.
Virtual Training nebo e-Learning?
Máme dostatečnou flexibilitu, takže vybírat můžete jak prezenční termíny, tak online kurzy.
Zkuste živý kurz virtuálněTarget group
- Data analysts
- qualitative experts
- others who want an introduction to SAS Enterprise Miner
Course structure
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
- 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.
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.
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Jsme vám k dispozici na telefonním čísle +420 222 553 101 vždy od pondělí do pátku: 9:00 - 17:00.
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