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. This course is appropriate for users of SAS Visual Analytics in [...]
  • SVSO35
  • Délka 2 dny
  • 0 ITK bodů
  • 1 termin
  • Praha (na vyžádání)

    Brno (na vyžádání)

    Bratislava (1 000 €)

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. This course is appropriate for users of SAS Visual Analytics in SAS Viya.

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

use SAS Visual Statistics to:

  • 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
  • Add splines to models
  • Perform decision tree modeling
  • Perform stratified model fitting
  • Perform model validation
  • Compare models
  • Generate score code

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.

  • 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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