SAS Enterprise Miner

Applied Analytics Using SAS Enterprise Miner

Learn To Discover Latest Technology This course is for participants who want to become experts in data mining and provide in depth data analytics using SAS Enterprise Miner.
  • Introduction to SAS Enterprise Miner
  • Data Preparation
  • Introduction to Predictive Modeling: Predictive Modeling Fundamentals and Decision Trees
    • Cultivating decision trees
    • Optimizing the complexity of decision trees
  • Introduction to Predictive Modeling: Regressions
    • Selecting regression inputs
    • Optimizing regression complexity
    • Interpreting regression models
    • Transforming inputs
    • Categorical inputs
  • Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools
    • Introduction to neural network models
    • Input selection
    • Stopped training
  • Model Assessment
    • Model fit statistics
    • Statistical graphics
    • Adjusting for separate sampling
    • Profit matrices
  • Model Implementation
    • Internally scored data sets
    • Score code modules
  • Introduction to Pattern Discovery
    • cluster analysis
    • market basket analysis (self-study)
  • Special Topics
    • Ensemble models
    • Surrogate models
    • SAS Rapid Predictive Modeler
  • Case Studies
    • Banking segmentation
    • Website usage associations
    • Credit risk
    • Enrollment management
Data analysts, qualitative experts, and others who want an introduction to SAS Enterprise Miner
  • Should be acquainted with Microsoft Windows and Windows software
  • Should have at least an introductory-level familiarity with basic statistics and regression modeling
  • Previous SAS software experience is helpful but not required
Learn skills that are required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models).
Delivery Method : Classroom Training / Live Web / Self Learning
Duration : 3 Days
Level : Advanced
Languages : English
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