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Applied Analytics Using SAS® Enterprise Miner™
AAEM : AAEM51
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.
- 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.
Live Class Schedule
Duration: 21 hours
Step into our live classes and experience a dynamic learning environment where you can ask questions, share ideas, and connect with your instructor and classmates. With on-demand lab hours, you can explore the material at your own pace. Our globally acclaimed instructors will motivate you to think bigger, so you can take what you've learned and achieve your biggest goals.
Live Class Schedule
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| 06-10 OCT 2025 | Live Web, US | 10:30 AM-3:30 PM EDT | Spanish | 3,000 USD | |
| 01-05 DEC 2025 | Live Web, US | 9:30 AM-2:30 PM EST | Spanish | 3,000 USD | |
Private Training
Get training tailored specifically for your team, led by expert SAS instructors. Choose from virtual sessions, or training at your location (or ours). Perfect for teams seeking a customized curriculum and plenty of interaction with a SAS specialist. We'll schedule it at a time that works for you.
Mentoring Services
Take your training to the next level with personalized mentoring. While private training offers structured coursework, mentoring provides hands-on, real-time support from a subject matter expert. As you work with your own data, you'll receive expert guidance to help you uncover insights, unlock the full potential of your data, and make faster progress. Perfect for those looking to apply what they’ve learned and see quicker results.