There is a new version of this course. Please see SAS Academy for Data Science: Advanced Analytics.

The SAS Certified Advanced Analytics Professional program represents Level 2 of the SAS Academy for Data Science. 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, as well as the essential communication skills necessary to deliver value to the organization.

The course work, hands-on practice, and projects help prepare students for the three exams needed to become a SAS Certified Advanced Analytics Professional. Exams are included.

Learn how to

• Apply machine learning and predictive modeling techniques to big distributed and in-memory data sets.
• Detect patterns in your data.
• Set up and run business experiments.
• Optimize your data.
• Use time-series forecasting techniques.
• Effectively communicate results and recommendations to executive-level management.

Who should attend

Individuals with a least six months of programming experience in SAS or another programming language, and at least six months of experience using mathematics, statistics, or both in a business environment. A master's degree or higher in a quantitative or technical field is recommended but not required.

Formats available	Standard duration
e-Learning:	210 hours/1 year license	System Requirements

To enroll in the advanced analytics certification program, you need at least six months of programming experience in SAS or another programming language, and at least six months of experience using mathematics, statistics, or both in a business environment. We also recommend that you first complete the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course, which is available as an instructor-led or free online e-learning course.

The curriculum includes the following courses:

• Applied Analytics Using SAS Enterprise Miner
• Neural Network Modeling
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• Predictive Modeling Using Logistic Regression
• Data Mining Techniques: Predictive Analytics on Big Data
• Using SAS to Put Open Source Models into Production
• Text Analytics Using SAS Text Miner
• Time Series Modeling Essentials
• Experimentation in Data Science
• Optimization Concepts for Data Science