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