This course is for experienced R users who want to apply their existing skills and extend them to the SAS environment. Emphasis is placed on programming and not statistical theory or interpretation

Students in this course should have knowledge of plotting, manipulating data, iterative processing, creating functions, applying functions, linear models, generalized linear models, mixed models, stepwise model selection, matrix algebra, and statistical simulations.

Learn how to

This course can help prepare you for the SAS Certified Specialist: Base Programming Using SAS 9.4 and SAS Certified Associate: Programming Fundamentals Using SAS 9.4

Learn how to

In this course Students learn a variety of machine learning and predictive modeling techniques, and how to apply the techniques to big distributed and in-memory data sets. Students also learn about experimentation in business, optimization, and time series forecasting

This self-paced learning is designed for individuals who need a solid foundation in using SAS software to conduct and interpret complex statistical data analysis and who can learn autonomously

The included content helps you prepare for the SAS Certified Statistical Business Analyst Using SAS(R)9: Regression and Modeling credential.

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The SAS Customer Intelligence common data model consists of tables that contain information about targeted customers, campaigns, and communications for reporting and follow-up marketing activities

The model also provides operational functionality that maintains real-time customer contact and response history data. For many administrators and users, the common data model is not fully understood. This course demystifies the common data model by presenting an in-depth explanation of its structure, use, and customization.

Prerequisites