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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Predictive Analytics on Big Data | 40% | - Working with distributed and in-memory data - High-performance modeling techniques - Scaling models for large datasets - SAS LASR Analytic Server and SAS Visual Analytics |
| Topic 2: Logistic Regression | 30% | - Model building and variable selection - Using SAS/STAT and SAS Enterprise Miner - Model assessment and validation - Interpretation of results |
| Topic 3: Neural Networks | 20% | - Implementation in SAS Enterprise Miner - Model evaluation and performance - Neural network architecture design - Training and tuning neural models |
| Topic 4: Open Source Models in SAS | 10% | - Integrating open source code with SAS - Deploying Python/R models in SAS environment - Productionalization of hybrid models |
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