SAS Predictive Modeling Course Training in Pune

Duration: 42 hrs | Live Web Training

This course covers the content of both SAS Data Integration Studio: Essentials and SAS Data Integration Studio: Additional Topics. It introduces and expands the knowledge of SAS Data Integration Studio and includes topics for registering sources and targets; creating and working with jobs; and working with transformations.
4.9 / 5.0 4.9 / 5.0

Course Curriculum

  • discussing descriptive statistics
  • discussing inferential statistics
  • listing steps for conducting a hypothesis test
  • discussing basics of using your SAS software
  • introducing to the SAS Enterprise Guide 7.1 environment
  • 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
  • understanding the concepts of multiple regression
  • building and interpreting models
  • describing all regression techniques
  • exploring stepwise selection techniques
  • examining residuals
  • investigating influential observations and collinearity
  • describing categorical data
  • examining tests for general and linear association
  • understanding the concepts of logistic regression and multiple logistic regression
  • performing backward elimination with logistic regression
  • 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)
  • Deployinging Jobs
  • selecting regression inputs
  • optimizing regression complexity
  • interpreting regression models
  • transforming inputs
  • categorical 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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