Congratulations!
You’ve completed the Graph Data Science in Practice workshop!
In just 4 hours, you’ve transformed from learning the basics to running sophisticated graph analytics at scale.
What You’ve Accomplished
Module 1: GDS Foundations
You mastered the fundamentals:
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The GDS Project → Run → Write workflow
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How to create graph projections for different analytical questions
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Algorithm categories and when to use each one
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Execution modes (stream, stats, mutate, write)
Module 2: Community Detection for Fraud
You applied algorithms to real fraud detection:
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Used Louvain to find densely connected fraud networks
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Applied WCC for deterministic community assignment
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Filtered noise with Degree Centrality
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Reduced search space by 98% while uncovering hidden fraud rings
Module 3: Python GDS Client
You built production-ready workflows:
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Connected to Neo4j and ran algorithms programmatically
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Analyzed citation networks with PageRank and Betweenness Centrality
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Detected research communities with Louvain
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Generated FastRP embeddings for machine learning pipelines
Module 4: Aura Graph Analytics
You scaled analytics to the cloud:
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Created ephemeral GDS Sessions for scalable workloads
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Optimized routes with Dijkstra’s shortest path
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Found alternative paths with Yen’s k-shortest paths
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Learned to run analytics without impacting production databases
You’re Now Ready To
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Apply GDS to your own datasets using the skills you’ve practiced
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Choose the right algorithms for different business problems
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Build production workflows with the Python GDS client
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Scale analytics with Aura Graph Analytics
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Identify fraud patterns, optimize logistics, analyze networks, and more
Your Next Steps
Ready to go deeper?
Continue Learning:
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Graph Data Science Fundamentals - Comprehensive coverage of all GDS concepts
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Applied Algorithms in GDS - Five industry use cases with deep algorithm coverage
Related Skills:
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Cypher Fundamentals - Master the query language
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Graph Data Modeling Fundamentals - Design effective graph structures
Resources:
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GDS Documentation - Complete algorithm reference
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Neo4j Community Forum - Connect with other practitioners
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GDS Developer Guide - Practical examples
Keep Practicing
Create a free Neo4j AuraDB instance or download Neo4j Desktop to continue applying these skills to your own data.
Thank You!
Thank you for completing the Graph Data Science in Practice workshop. We hope you enjoyed the hands-on experience and feel confident applying graph algorithms to solve real-world problems.
Now go build something amazing with graph data science!
Summary
You’ve successfully completed all 27 lessons across 4 modules:
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8 lessons on GDS foundations and workflow
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5 lessons on community detection for fraud
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9 lessons on Python GDS client workflows
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5 lessons on Aura Graph Analytics and pathfinding
You’re now equipped to apply graph algorithms to real business problems, build production workflows, and scale analytics with confidence.