Course Duration
48 hrs
Mode Of Training
Live Web
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SAS Certified Trainer
Course hours 48 hrs
Training Mode Live Web
100% Job Assistance
SAS Certified Trainer
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This course is appropriate for SAS Enterprise Miner from release 5.3 up to 14.2. The course covers the 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).
define a SAS Enterprise Miner project and explore data graphically
modify data for better analysis results
build and understand predictive models such as decision trees and regression models
compare and explain complex models
generate and use score code
apply association and sequence discovery to transaction data.
Data analysts
Aualitative experts, and others who want an introduction to SAS Enterprise Miner
Before attending this course, you should be acquainted with Microsoft Windows and Windows software.
In addition, you should have at least an introductory-level familiarity with basic statistics and regression modeling.
Previous SAS software experience is helpful but not required.
This course addresses SAS Enterprise Miner software.
Introduction
Accessing and Assaying Prepared Data
Introduction to Predictive Modeling: Predictive Modeling Fundamentals and Decision Trees
Introduction to Predictive Modeling: Regressions
Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools
Model Assessment
Model Implementation
Introduction to Pattern Discovery
Special Topics
Case Studies
Training & Digital Badge.
Course material.
2 attempts of Global certification.
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▪ discussing fundamental statistical concepts
▪ examining distributions
▪ describing categorical data
▪ constructing confidence intervals
▪ performing simple tests of hypothesis
▪ performing one-way ANOVA
▪ performing multiple comparisons
▪ performing two-way ANOVA with and without interactions
▪ using exploratory data analysis
▪ producing correlations
▪ fitting a simple linear regression model
▪ understanding the concepts of multiple regression
▪ building and interpreting models
▪ describing all regression techniques
▪ exploring stepwise selection techniques
▪ describing categorical data
▪ examining tests for general and linear association
▪ understanding the concepts of logistic regression and multiple logistic regression
▪ exploring logit plots (Self-Study)
▪ examining residuals
▪ investigating influential observations and collinearity
▪ Introduction
▪ introduction to SAS Enterprise Miner
▪ creating a SAS Enterprise Miner project, library, and diagram
▪ defining a data source
▪ exploring a data source
▪ cultivating decision trees
▪ optimizing the complexity of decision trees
▪ understanding additional diagnostic tools (self-study)
▪ autonomous tree growth options (self-study)
▪ selecting regression inputs
▪ optimizing regression complexity
▪ interpreting regression models
▪ transforming inputs
▪ polynomial regressions (self-study)
▪ introduction to neural network models
▪ input selection
▪ stopped training
▪ other modeling tools (self-study)
▪ model fit statistics
▪ statistical graphics
▪ adjusting for separate sampling
▪ profit matrices
▪ internally scored data sets
▪ score code modules
▪ cluster analysis
▪ market basket analysis (self-study)
▪ ensemble models
▪ variable selection
▪ categorical input consolidation
▪ surrogate models
▪ SAS Rapid Predictive Modeler
▪ banking segmentation case study
▪ website usage associations case study
▪ credit risk case study
▪ enrollment management case study
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