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.

Learn how to 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.
• Perform decision tree modeling.
• Perform stratified model fitting.
• Perform model validation.
• Compare models.
• Generate score code.

Who should attend

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

Formats available	Standard Duration (duration can vary, see event schedule for details)
Classroom:	2.0 days
Live Web Classroom:	4 half-day session(s)	System Requirements
e-Learning:	14 hours/180 day

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.

This course addresses SAS Visual Statistics, SAS Viya software.

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.