Certified and Advanced Degree
Computer Instructors and Consultants
Course Catalog
Data Mining: Principles and Best Practices
Code: BELDR
Duration: 3 Day
$2475 USD

OVERVIEW

Data mining is an advanced science that can be difficult to do correctly. In this course, you will learn about the power and potential of data mining and how to discover useful patterns and trends from data. You will also learn 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.

Note: 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.

DELIVERY FORMAT

This course is available in the following formats:

Classroom

Duration: 3 Day

CLASS SCHEDULE
Call 800-798-3901 to enroll in this class!

GOALS
OUTLINE

1. Executive Summary

  • Introduction to data mining
  • SAS Enterprise Miner as a data mining platform

2. Learning Strategies

3. Machine Learning Algorithms I

4. Model Application

  • Mining process
  • Fraud detection
  • Cumulative response charts
  • Cutoff thresholds

5. Model Validation

  • Ways models fail
  • Out-of-time test sample
  • Overfit
  • Cross validation

6. Machine Learning Algorithms II

  • Neural networks
  • Target shuffling
  • Regression models
  • Decision trees

7. 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

8. Top Ten Data Mining Mistakes

9. Visualization (Self-Study)

1. Executive Summary

  • Introduction to data mining
  • SAS Enterprise Miner as a data mining platform

2. Learning Strategies

3. Machine Learning Algorithms I

4. Model Application

  • Mining process
  • Fraud detection
  • Cumulative response charts
  • Cutoff thresholds

5. Model Validation

  • Ways models fail
  • Out-of-time test sample
  • Overfit
  • Cross validation

6. Machine Learning Algorithms II

  • Neural networks
  • Target shuffling
  • Regression models
  • Decision trees

7. 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

8. Top Ten Data Mining Mistakes

9. Visualization (Self-Study)

LABS

Exercises or hands-on workshops are included with most SAS courses.

Exercises or hands-on workshops are included with most SAS courses.

WHO SHOULD ATTEND

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

PREREQUISITES

None