Learning Path

Network Science and Graph Algorithms for Data Scientists

Graph Theory Fundamentals → Capstone Project: End-to-End Graph Analysis

12 Knowledge Nodes · 12 Learning Steps · v0 Active

This advanced graduate-level path equips data scientists with the knowledge and skills to analyze graph-structured data using network science and graph algorithms. Starting with foundational graph theory and Python tools, it progresses through network metrics, centrality, community detection, graph embeddings, graph neural networks, and knowledge graphs, culminating in practical application with NetworkX and Neo4j. The path emphasizes hands-on learning and real-world data analysis.

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Learning Route

12 learning steps · 3 phases. Click any step to inspect it and see it on the Knowledge Map.

Phase 1

Foundation

4 steps

Phase 2

Core Concepts

4 steps

Phase 3

Advanced Topics

4 steps