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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Manage Data Sets | 20% | - Read and write data using Microsoft R Server - Transform and clean data - Handle large datasets beyond memory |
| Topic 2: Build Statistical and Machine Learning Models | 25% | - Process text data with RML packages - Train linear and generalized linear models - Use partitioning and parallelized models |
| Topic 3: Deploy and Operationalize R Solutions | 15% | - Integrate R with SQL Server R Services - Manage scalable analytics workflows - Run R scripts in Hadoop/Spark environments |
| Topic 4: Evaluate and Optimize Models | 20% | - Optimize R scripts for scale and speed - Assess model performance - Tune model parameters |
| Topic 5: Explore and Visualize Data | 20% | - Create visualizations using R and RevoScaleR - Perform exploratory data analysis - Summarize and aggregate data |
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