Congratulations!
You’ve completed Getting Started with Graph Data Science.
You’ve built a strong foundation in GDS that will enable you to apply graph algorithms to solve complex analytical problems.
What you’ve learned
In this course, you learned:
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What graph data science is: How GDS extends graph databases with analytical algorithms to find patterns, communities, and insights invisible to traditional data science
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The GDS workflow: The Project → Run → Write pattern that underlies all GDS work
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Graph projections: How to create monopartite, bipartite, and multipartite projections tailored to specific analytical questions
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Graph catalog operations: How to list, validate, and manage projected graphs in memory
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Algorithm categories: The five main categories (Centrality, Community Detection, Pathfinding, Similarity, Embeddings) and when to use each
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Algorithm execution modes: How to use stats, stream, mutate, write, and estimate modes for different workflow needs
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Working with results: How to use
gds.util.asNode()and related functions to access node properties from algorithm outputs -
Reading GDS documentation: How to independently learn any GDS algorithm by understanding syntax, configuration, and attributes
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Algorithm configuration: How to customize algorithms with orientation (directed/undirected), weights, and algorithm-specific parameters
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Relationship aggregation: How to collapse multiple relationships into weighted connections using
count(r)during projection -
Projection modeling: How to design projections based on analytical questions, matching algorithm assumptions to graph structure
The skills you have
You can now:
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Understand what graph data science is and how it differs from traditional analytics
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Create graph projections tailored to specific analytical questions
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Manage graphs in the GDS catalog (list, validate, drop)
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Choose appropriate algorithms for centrality, community, pathfinding, similarity, and embedding analyses
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Run algorithms in different execution modes based on workflow needs
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Configure algorithms with orientation, weights, and custom parameters
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Read and understand GDS documentation independently
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Access and interpret algorithm results using utility functions
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Design projections that match algorithm requirements
Continue your learning
Ready to apply these fundamentals to real-world problems?
Continue your journey with Applied Algorithms in GDS, where you’ll see these techniques solve actual industry challenges:
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Manufacturing optimization: Root cause analysis using centrality and community detection
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Fraud detection: Network-based fraud identification with graph patterns
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Supply chain logistics: Route optimization with pathfinding algorithms
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Citation networks: Research influence mapping and analysis
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Node embeddings: Structural similarity and machine learning workflows
Each module demonstrates not just how to run algorithms, but when and why professionals choose specific approaches to solve complex problems.
Additional resources
Want to dive deeper into specific topics?
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Cypher Fundamentals - Master the query language used in GDS projections
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Graph Data Modeling Fundamentals - Learn to design effective graph structures
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GDS Documentation - Comprehensive algorithm reference and guides
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Neo4j Community Forum - Connect with other GDS practitioners
You can create a free Neo4j AuraDB instance or download Neo4j Desktop to continue practicing with your own data.
Summary
You’ve completed your journey through the fundamentals of Graph Data Science. You understand projections, algorithms, execution modes, and how to work independently with GDS documentation.
You’re ready to apply these skills to real-world problems in the Applied Algorithms course.