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