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SAS(R) Visual Statistics in SAS(R) Viya(R): Interactive Model Building

This course introduces SAS Visual Statistics for building predictive models in an interactive, exploratory way. Exploratory model fitting is a critical step in modeling big data. The classroom and...

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$1,600 USD GSA  $1,314.86
Course Code SVSO35
Duration 2 days
Available Formats Classroom, Virtual
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This course introduces SAS Visual Statistics for building predictive models in an interactive, exploratory way. Exploratory model fitting is a critical step in modeling big data.

The classroom and Live Web course is appropriate for users of SAS Visual Analytics in SAS Viya 3.5. In e-learning, there is a course for users of SAS Visual Analytics in SAS Viya 3.5, and there is also a course for users of SAS Visual Analytics in SAS Viya 2020.1.

Skills Gained

  • Perform statistical analysis of data of any size.
  • Create a report with pages.
  • Determine useful preferences and settings.
  • Create segments, or clusters, of input variables.
  • Perform regression and logistic regression modeling.
  • Perform decision tree modeling.
  • Perform stratified model fitting.
  • Perform model validation.
  • Compare models.
  • Generate score code.

Who Can Benefit

  • Predictive modelers, business analysts, and data scientists who want to take advantage of SAS Visual Statistics for highly interactive, rapid model fitting

Prerequisites

  • Before attending this course, you should have an understanding of regression and logistic regression analysis for predictive modeling. You can gain this knowledge from the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course. You should also have experience using SAS Visual Analytics, which you can gain from the SAS® Visual Analytics 1 for SAS® Viya®: Basics course.

Course Details

Introduction to SAS Visual Statistics

  • Managing reports and pages.
  • SAS Viya architecture.

Cluster Segmentation

  • Segmentation concepts.
  • Cluster analysis.

Models with Continuous Targets

  • Linear regression models.
  • Generalized linear models.
  • Generalized additive models.
  • Model validation.

Models with Categorical Targets

  • Logistic regression.
  • Modeling with group-by variables.
  • Decision trees.
  • Decision trees in SAS Visual Statistics.

Model Comparison and Scoring

  • Comparing models.
  • Scoring.
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