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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Hadoop Ecosystem and NoSQL | 15% | - Data models and scalable storage solutions - HDFS, YARN, MapReduce architecture - Hive, Pig, Spark processing frameworks - NoSQL databases: Cassandra, MongoDB |
| Topic 2: Data Science Theory and Methods | 15% | - Model evaluation and validation metrics - Ethics and data governance - Regression, classification, clustering algorithms - Statistical inference and hypothesis testing |
| Topic 3: Social Network Analysis (SNA) | 23% | - Centrality, connectivity, and influence measures - Graph databases and network visualization - Community detection algorithms - Graph theory and network metrics |
| Topic 4: Advanced Analytics Implementation | 15% | - Machine learning model deployment - Distributed computing and optimization - Performance tuning and scalability |
| Topic 5: Data Visualization | 12% | - Visualization principles and best practices - Interactive dashboards and reporting - Chart selection and data storytelling - Tools for visualization implementation |
| Topic 6: Natural Language Processing (NLP) | 20% | - NLP pipelines and applications - Sentiment analysis and topic modeling - Text preprocessing and feature extraction - Named entity recognition |
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