Business Knowledge Series course

Presented by John Elder IV, Ph.D., President, Elder Research Inc.; or Gerhard Pilcher, Senior Scientist, Elder Research Inc.; or Mike Thurber, Senior Data Miner, Elder Research Inc.

Data mining is an advanced science that can be difficult to do correctly. This course introduces you to the power and potential of data mining and shows you how to discover useful patterns and trends from data. Valuable practical advice, acquired during years of real-world experience, focuses on how to properly build reliable predictive models and interpret your results with confidence. Examples are drawn from several industries, including credit scoring, fraud detection, biology, investments, and cross-selling.

This course is not hands-on training for SAS Enterprise Miner software, although SAS Enterprise Miner is used by the instructor to illustrate specific modeling techniques and by students for their classroom exercises.

Learn how to

• identify projects with a high probability of success
• translate a business problem into a closely related set of technical tasks
• transform raw data to create higher-order features that reveal information
• build models using powerful nonlinear techniques, such as decision trees and neural networks
• use multiple re-sampling techniques to avoid overfit and predict how well models will perform in actual use
• interpret and validate modeling results
• become aware of the top analytic mistakes and how to avoid them.

Who should attend

Those who have a strong interest in solving a business problem, and who have a technical background, especially familiarity with computer programming and statistics

Formats available	Duration
Classroom:	3.0 days

Before attending this course, you should

• have experience with basic modeling, such as regression
• preferably, have prior exposure to Base SAS coding, though it is not required
• preferably have exposure to SAS Enterprise Miner, though it is not required.

For comprehensive, hands-on training on SAS Enterprise Miner, attend the Applied Analytics Using SAS Enterprise Miner course.

This course addresses SAS Enterprise Miner software.

Executive Summary

• introduction, executive summary of data mining
• SAS Enterprise Miner as a data mining platform

Learning Strategies

Machine Learning Algorithms I

Model Application

• mining process
• fraud detection
• cumulative response charts
• cutoff thresholds

Model Validation

• ways models fail
• out-of-time test sample
• overfit
• cross validation

Machine Learning Algorithms II

• neural networks
• target shuffling
• regression models
• decision trees

Ensembles

• ensembles
• weaknesses of a single model
• bagging and boosting
• academic example: trees with bags of five versus eight nodes
• real-world example: credit scoring

Top Ten Data Mining Mistakes

Visualization (Self-Study)