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Dauer:  3 Tage
Code:  BELDR
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Data Mining: Principles and Best Practices
 

Inhalte und Termine dieses Kurses haben sich geändert. Bitte klicken Sie hier, um zur neuen Beschreibung zu kommen.


 
Kursziel
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 a 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.
 
Voraussetzungen
Before attending this course, you should have experience with basic modeling, such as regression. Preferably, you should have prior exposure to Base SAS coding, though it is not required. Having exposure to SAS Enterprise Miner can be helpful, though it is also not required..
 
Zielgruppe
Those who have a strong interest in solving a business problem, and who have a technical background, especially familiarity with computer programming and statistics
 
Module
SAS Enterprise Miner Software
 
Kursinhalte
  • 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)

 
Referent
This course will be presented by John Elder IV, Ph.D., President; or Gerhard Pilcher, Senior Scientist; or Mike Thurber, Senior Data Miner, Elder Research Inc.

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