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Applied Analytics Using SAS Enterprise Miner
30 Hours
Online Instructor-led Training
USD 1399 (
About this Course
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 15.1.
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.
Target Audience
Data analysts, qualitative experts, and others who want an introduction to SAS Enterprise Miner
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Applied Analytics Using SAS Enterprise Miner
Course Details & Curriculum
Introduction
- Introduction to SAS Enterprise Miner.
- Creating a SAS Enterprise Miner project, library, and diagram.
- Defining a data source.
- Exploring a data source.
- Introduction.
- Cultivating decision trees.
- Optimizing the complexity of decision trees.
- Understanding additional diagnostic tools (self-study).
- Autonomous tree growth options (self-study).
- Selecting regression inputs.
- Optimizing regression complexity.
- Interpreting regression models.
- Transforming inputs.
- Categorical inputs.
- Polynomial regressions (self-study).
- Input selection.
- Stopped training.
- Other modeling tools (self-study).
- Model fit statistics.
- Statistical graphics.
- Adjusting for separate sampling.
- Profit matrices.
- Internally scored data sets.
- Score code modules.
- Cluster analysis.
- Market basket analysis (self-study).
- Ensemble models.
- Variable selection.
- Categorical input consolidation.
- Surrogate models.
- SAS Rapid Predictive Modeler.
- Banking segmentation case study.
- Website usage associations case study.
- Credit risk case study.
- Enrollment management case study.
