In this course, you learn to use the R API to take control of SAS Cloud Analytic Services (CAS) actions from Jupyter Notebook.

You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models in CAS using familiar R functionality via the SWAT (SAS Wrapper for Analytics Transfer) package.

You then learn to download results to the client and use native R syntax to compare models.

Who should attend:

R users who want to take advantage of SAS Viya distributed analytics for fast and efficient modeling


Before taking this course, you should have experience writing R programs for data analytics.

There are no SAS prerequisites.

This course addresses SAS Viya, SAS Visual Data Mining and Machine Learning software.

Delegates will learn how to

Learn how to:

  • Use the R API in SAS Viya.
  • Submit CAS actions from R.
  • Manage, alter, and prepare data on the CAS server.
  • Implement and compare machine learning models on the CAS server.
  • Move data between the client and server.
  • Use R syntax to wrap up CAS actions with functions and loops.
  • Promote data to persist in memory.


SAS Viya and Open Source Integration

  • SAS Viya and Cloud Analytic Services (CAS).
  • Open source development interfaces.
  • Scripting Wrapper for Analytics Transfer (SWAT).
  • Fundamentals of the R and Python APIs.

Machine Learning

  • Predictive modeling.
  • Predictive models.
  • Model assessment.