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SPSS IBMSPSSMBPDA Exam Syllabus Topics:
| Section | Objectives |
| Topic 1: Model Evaluation and Validation | - Model performance metrics
- 1. Accuracy and confusion matrix
- 2. ROC and lift charts
- Model validation techniques
- 1. Training and testing split
- 2. Cross-validation concepts
|
| Topic 2: Predictive Modeling | - Clustering and segmentation
- 1. Customer segmentation use cases
- 2. K-means clustering
- Classification techniques
- 1. Decision trees
- 2. Logistic regression basics
|
| Topic 3: Data Preparation and Transformation | - Data cleansing techniques
- 1. Outlier detection and treatment
- 2. Handling missing values
- Data transformation methods
- 1. Deriving new fields
- 2. Normalization and aggregation
|
| Topic 4: Business Application of Analytics | - Deployment considerations
- 1. Reporting and insights delivery
- 2. Operationalizing models
- Business problem framing
- 1. Translating business needs into models
- 2. Use case identification
|
| Topic 5: IBM SPSS Modeler Fundamentals | - Data understanding and profiling
- 1. Data quality assessment
- 2. Data types and structures
- Introduction to SPSS Modeler interface
- 1. Streams and nodes overview
- 2. Data preparation basics
|