SAS Text Analytics, Time Series, Experimentation and Optimization

Exam Content Guide

Text Analytics - 30%

• Create data sources for text mining
• Import data into SAS Text Analytics
• Use text mining to support forensic linguistics using stylometry techniques
• Retrieve information for Analysis
• Parse and quantify Text
• Perform predictive modeling on text data
• Use the High-Performance (HP) Text Miner Node

Time Series - 30%

• Identify and define time series characteristics, components and the families of time series models
• Diagnose, fit, and interpret ARIMAX Models
• Diagnose, fit, and interpret Exponential Smoothing Models
• Diagnose, fit, and interpret Unobserved Components Models

Experimentation & Incremental Response Models - 20%

• Explain the role of experiments in answering business questions
• Relate experimental design concepts and terminology to business concepts and terminology
• Explain how incremental response models can identify cases that are most responsive to an action
• Use the Incremental Response node in SAS Enterprise Miner

Optimization - 20%

• Optimize linear programs
• Optimize nonlinear programs