Video description
This course introduces Enterprise Miner while demonstrating two common applications: segmentation and predictive modeling. It starts with a brief overview of the software and then covers segmentation and predictive modeling using a case-study approach based on real-world data. Upon completing the course, learners will have a basic, working knowledge of how to use Enterprise Miner to perform data mining and machine learning tasks. Participants should have a quantitative background and (ideally) some basic understanding of predictive models, including regression.
- Learn how to use Enterprise Miner to perform data mining and machine learning tasks
- Explore the fundamentals of predictive modeling and clustering
- Discover how to build, compare, and deploy predictive models using SAS Enterprise Miner
- Learn how to perform, interpret, and profile a cluster analysis using SAS Enterprise Miner
Jeffrey Thompson is a Senior Analytical Training Consultant with the SAS Institute and has worked with SAS since the early 90s. A former associate professor of statistics at North Carolina State University, Jeffrey has been published in the International Statistical Review, the Austrian Journal of Statistics, and other peer-reviewed journals. He holds a bachelor's degree in mathematics, a master's degree in statistical computing, and a PhD in statistics.
Table of Contents
Chapter 1: Introduction
Welcome to the Course
About the Author
Chapter 2: Introduction to SAS Enterprise Miner
The Enterprise Miner Interface
The SEMMA Approach
Analytical Workflow and Enterprise Miner Strengths
Chapter 3: Accessing and Assaying Prepared Data
Defining a Data Source and Application for the First Demonstration
Demo: Opening Enterprise Miner, Opening a Project, and Setting Sampling Preferences
Demo: Creating a Data Source in Enterprise Miner
Demo: Changing Metadata
Demo: Exploring Data
Chapter 4: Introduction to Pattern Discovery
Introduction to Pattern Discovery and Applications
Segmentation
Demo: Opening a Diagram and bringing a Data Source into a Process Flow
Demo: Filtering out Unwanted Cases
Demo: Setting up and Running the Cluster Node
Demo: Results of the Cluster Node
Demo: Profiling the Clusters
Chapter 5: Introduction to Predictive Modeling
Introduction to Predictive Modeling and Application for the Second Demonstration
Predictive Modeling Essentials
Demo: Opening a Diagram and Exploring Data
Demo: Partitioning Data
Demo: Building and Discussing a Decision Tree
Demo: Imputation and Setting up Regression and Neural Network Models
Demo: Running Regression and Neural Network Models and Model Comparison
Chapter 6: Model Implementation
Model Implementation
Demo: Creating a Scoring Data Source
Demo: Internally Scoring New Data
Demo: Exploring Exported Data
Demo: SAS Score Code and Java and C Score Code
Chapter 7: Conclusion
Wrap Up and Thank You