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SASInstitute A00-226 Exam Syllabus Topics:

SectionWeightObjectives
Text Analytics30%- Text Analytics Applications
  • 1. Predictive modeling with text features
    • 2. Text Rule Builder and SVD-based reduction
      - Text Mining Data Preparation
      • 1. Create and prepare text data sources for analysis
        • 2. Import and merge text and structured datasets
          - Text Processing and Modeling
          • 1. Tokenization, parsing, and feature extraction
            • 2. Topic modeling and clustering
              Experimentation and Incremental Response Models20%- Incremental Response Modeling
              • 1. Net Information Value (NIV) and WOE concepts
                • 2. Treatment vs control group analysis
                  - Experimental Design
                  • 1. Design of Experiments (DOE) principles
                    • 2. Factorial vs OFAT designs
                      - Business Experimentation
                      • 1. Marketing campaign response modeling
                        • 2. Nuisance variable control and blocking
                          Time Series30%- Forecasting and Evaluation
                          • 1. Forecast accuracy metrics and validation
                            • 2. Holdout sample evaluation
                              - Time Series Modeling
                              • 1. Unobserved components models
                                • 2. Exponential smoothing models
                                  • 3. ARIMA and ARIMAX models
                                    - Time Series Fundamentals
                                    • 1. Stationarity and transformations
                                      • 2. Components: trend, seasonality, noise, cycle
                                        Optimization20%- Linear Programming
                                        • 1. OPTMODEL procedure usage
                                          • 2. Formulating optimization problems
                                            - Advanced Optimization Techniques
                                            • 1. Constraints, decision variables, and parameter modeling
                                              • 2. Data Envelopment Analysis (DEA)

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