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Getting Started with SAS Enterprise Miner for Machine Learning

Getting Started with SAS Enterprise Miner for Machine Learning

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by Jeff Thompson
Publisher: O'Reilly Media, Inc.
Release Date: February 2018
ISBN: 9781492028406
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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