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SASInstitute A00-226 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Optimization20%- Linear Programming
  • 1. Formulating optimization problems
    • 2. OPTMODEL procedure usage
      - Advanced Optimization Techniques
      • 1. Data Envelopment Analysis (DEA)
        • 2. Constraints, decision variables, and parameter modeling
          Topic 2: Time Series30%- Time Series Fundamentals
          • 1. Components: trend, seasonality, noise, cycle
            • 2. Stationarity and transformations
              - Time Series Modeling
              • 1. Unobserved components models
                • 2. Exponential smoothing models
                  • 3. ARIMA and ARIMAX models
                    - Forecasting and Evaluation
                    • 1. Holdout sample evaluation
                      • 2. Forecast accuracy metrics and validation
                        Topic 3: Experimentation and Incremental Response Models20%- Business Experimentation
                        • 1. Marketing campaign response modeling
                          • 2. Nuisance variable control and blocking
                            - Experimental Design
                            • 1. Design of Experiments (DOE) principles
                              • 2. Factorial vs OFAT designs
                                - Incremental Response Modeling
                                • 1. Net Information Value (NIV) and WOE concepts
                                  • 2. Treatment vs control group analysis
                                    Topic 4: Text Analytics30%- Text Processing and Modeling
                                    • 1. Topic modeling and clustering
                                      • 2. Tokenization, parsing, and feature extraction
                                        - Text Analytics Applications
                                        • 1. Predictive modeling with text features
                                          • 2. Text Rule Builder and SVD-based reduction
                                            - Text Mining Data Preparation
                                            • 1. Import and merge text and structured datasets
                                              • 2. Create and prepare text data sources for analysis

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