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Last Updated: Aug 15, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment & Evaluation Basics | 10% | - Model assessment metrics - Exporting results and reports |
| Topic 2: Introduction to IBM SPSS Modeler & CRISP-DM | 15% | - SPSS Modeler interface and workbench - CRISP-DM methodology |
| Topic 3: Data Preparation & Transformation | 35% | - Data caching and optimization - Restructuring, aggregating and sampling data - Creating derived variables and reclassification - Handling missing values and outliers |
| Topic 4: Data Understanding & Exploration | 25% | - Loading and inspecting data sources - Identifying data quality issues - Descriptive statistics and visualization |
| Topic 5: Modeling Fundamentals | 15% | - Using basic predictive nodes - Interpreting initial model outputs - Supervised and unsupervised modeling concepts |
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