When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
SAS(R) 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 as...
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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 as well as 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.
The coursework, hands-on practice, and projects help prepare students for the three exams needed to become a SAS Certified Advanced Analytics Professional.
Skills Gained
- 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 Can Benefit
- 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
Prerequisites
- 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 or statistics (or both) in a business environment. A master's degree or higher in a quantitative or technical field is recommended but not required. We also recommend that you first complete the &stat1 course, which is available as an instructor-led or free online e-learning course.
Course Details
The curriculum includes the following courses:
- Applied Analytics Using SAS® Enterprise Miner™
- Neural Network Modeling
- Communicating Technical Findings with a Non-Technical Audience
- 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