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Data Mining: Principles and Best Practices

Course Details
Tuition (USD): $2,475.00 • Classroom (3 days)
Course Details
GSA (USD): $2,244.33 • Classroom (3 days)
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.

Skills Gained

  • 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 Can Benefit

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


  • 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.

Course Details

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
  • 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)

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