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Applied Analytics Using SAS Enterprise Miner
This course is appropriate for SAS Enterprise Miner from release 5.3 up to 14.2.
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About Program
Format of Training
Training Features
Course Curriculum
Course Fees
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This course is appropriate for SAS Enterprise Miner from release 5.3 up to 14.2. The 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 can help prepare you for the following certification exam(s): Predictive Modeling Using SAS Enterprise Miner.
Format of Training
Taught by certified instructors at high-tech facilities across the country
- A SAS expert at your side.
- Focused learning away from the office
- Networking opportunities
- State-of-the-art facilities
- Electronic course notes downloadable to your device and permission to print
- Business Knowledge Series: in-depth courses on the latest business topics
- We offer Connected Classes! Watch for courses in Cary, New York, Arlington, Dallas and San Francisco that connect remote students via our Live Web classroom.
All the benefits of the classroom without the travel
- Join the classroom right from your desktop
- Led by an expert instructor who can virtually look over your shoulder
- Ask questions and get answers in real-time
- Access the latest software via a virtual lab
- Receive 20 business days' access to a recording of your course
- Discuss, share, exchange ideas with participants from different countries
Training Features
- 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.
Course Curriculum
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
Course Fees
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