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Getting Started with SAS Enterprise Miner for Machine Learning

Video Description

SAS Enterprise Miner is SAS's premier tool for data mining and predictive modeling. 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

  1. Chapter 1: Introduction
    1. Welcome to the Course 00:01:33
    2. About the Author 00:01:12
  2. Chapter 2: Introduction to SAS Enterprise Miner
    1. The Enterprise Miner Interface 00:05:40
    2. The SEMMA Approach 00:07:36
    3. Analytical Workflow and Enterprise Miner Strengths 00:02:24
  3. Chapter 3: Accessing and Assaying Prepared Data
    1. Defining a Data Source and Application for the First Demonstration 00:03:47
    2. Demo: Opening Enterprise Miner, Opening a Project, and Setting Sampling Preferences 00:02:56
    3. Demo: Creating a Data Source in Enterprise Miner 00:07:44
    4. Demo: Changing Metadata 00:03:19
    5. Demo: Exploring Data 00:06:13
  4. Chapter 4: Introduction to Pattern Discovery
    1. Introduction to Pattern Discovery and Applications 00:07:01
    2. Segmentation 00:03:37
    3. Demo: Opening a Diagram and bringing a Data Source into a Process Flow 00:02:19
    4. Demo: Filtering out Unwanted Cases 00:04:33
    5. Demo: Setting up and Running the Cluster Node 00:03:05
    6. Demo: Results of the Cluster Node 00:05:50
    7. Demo: Profiling the Clusters 00:10:03
  5. Chapter 5: Introduction to Predictive Modeling
    1. Introduction to Predictive Modeling and Application for the Second Demonstration 00:06:51
    2. Predictive Modeling Essentials 00:12:02
    3. Demo: Opening a Diagram and Exploring Data 00:09:48
    4. Demo: Partitioning Data 00:05:26
    5. Demo: Building and Discussing a Decision Tree 00:09:18
    6. Demo: Imputation and Setting up Regression and Neural Network Models 00:08:38
    7. Demo: Running Regression and Neural Network Models and Model Comparison 00:06:21
  6. Chapter 6: Model Implementation
    1. Model Implementation 00:01:36
    2. Demo: Creating a Scoring Data Source 00:03:46
    3. Demo: Internally Scoring New Data 00:03:25
    4. Demo: Exploring Exported Data 00:03:00
    5. Demo: SAS Score Code and Java and C Score Code 00:04:05
  7. Chapter 7: Conclusion
    1. Wrap Up and Thank You 00:00:37