Data Mining Techniques

This course provides the students with the skills necessary to set up, execute, and interpret the output from data mining analysis tools. This course is based on the following book: Data Mining Techniques, 3rd Edition, by Gordon S. Linoff and Michael J. A. Berry, published April, 2011 by Wiley Publishing, Inc, ISBN: 978-0-470-65093-6. The course is usually taught in one, two, or three days. Roughly 7 chapters are taught per day. The first 7 chapters are taught in 1 day, 14 chapters are taught in 2 days, and all 21 chapters are taught in 3 days. If the students wish, the instructor and the students may decide to skip around the chapters.
Students should have at least some experience with coding SQL for any relational database management system plus at least a conceptual understanding of Data Warehousing.
1-3 Days/Lecture & Lab
This course is intended for users, power users, programmers, analysts, DBAs, Data Modelers, or anyone else who needs to do data mining
  • What Is Data Mining and Why Do It?
  • Data Mining Applications in Marketing and Customer Relationship Management
  • The Data Mining Process
  • What You Should Know About Data
  • Descriptions and Prediction Profiling and Predictive Modeling
  • Data Mining Using Classic Statistical Techniques
  • Decision Trees
  • Artificial Neural Networks
  • Nearest Neighbor Approaches Memory-Based Reasoning and Collaborative Filtering
  • Knowing When to Worry Using Survival Analysis to Understand Customers
  • Genetic Algorithms and Swarm Intelligence
  • Tell Me Something New Pattern Discovery and Data Mining
  • Alternative Approaches to Cluster Detection
  • Market Basket Analysis and Association Rules
  • Link Analysis
  • Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining
  • Building Customer Signatures
  • Derived Variables Making the Data Mean More
  • Too Much of a Good Thing? Techniques for Reducing the Number of Variables
  • Listen Carefully to What Your Customers Say Text Mining

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