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Bite-Sized Neo4j for Data Scientists

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Overview

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This course teaches data scientists to work with Neo4j from Python and Jupyter, including data loading, Cypher querying, graph projections, visualization, graph algorithms, and embeddings for machine learning. It also compares graph queries and calculations with SQL workflows.

Syllabus

Part 1: Bite-Sized Neo4j for Data Scientists - Connect from Jupyter to a Neo4j Sandbox.
Part 2: Bited-Sized Neo4j for Data Scientists - Using the py2neo Python Driver.
Part 3: Bite-Sized Neo4j for Data Scientists - Using the Neo4j Python Driver.
Part 4: Bite-Sized Neo4j for Data Scientists - Basic Cypher Queries (and with Google Colab).
Part 5: Bite-Sized Neo4j for Data Scientists - Populating the Database from Pandas.
Part 6: Bite-Sized Neo4j for Data Scientists - Populating the Database with LOAD CSV.
Part 7: Bite-Sized Neo4j for Data Scientists - Populating the Database with the neo4j-admin tool.
Part 8: Populating the Database from a JSON file.
Part 9: Cypher Queries 2.
Part 10: Creating in-memory graphs with Cypher projections.
Part 11: Import RDF data from Wikidata.
Part 12: Creating In-Memory Graphs with Native Projections.
Part 13: Calculating Centrality.
Part 14: Community Detection with the Louvain Method.
Part 15: Community detection via Weakly Connected Components.
Part 16: Using Strongly Connected Components to find Communities.
Part 17: Creating FastRP Graph Embeddings.
Graph Data Visualization for Data Scientists and Data Analysts | Neo4j Bloom.
Part 18: Bite-Sized Neo4j for Data Scientists - Putting Graph Embeddings into an ML Model.
Part 19: Starting with a SQL table....
Part 20: ...And compare it to a graph... (2/n).
Part 21: An example of when querying a graph can be easier than SQL (3/n).
Part 22: A side-by-side calculation of degree using SQL and Neo4j (4/n).

Taught by

Neo4j

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