Bite-Sized Neo4j for Data Scientists

Overview

Dive into a comprehensive tutorial series designed for data scientists to master Neo4j, a popular graph database management system. Learn to connect Jupyter to a Neo4j Sandbox, utilize Python drivers like py2neo and Neo4j Python Driver, and execute basic Cypher queries. Explore techniques for populating the database from various sources including Pandas, CSV files, JSON files, and the neo4j-admin tool. Advance your skills with in-memory graph creation, RDF data import from Wikidata, and implementation of graph algorithms for centrality calculation and community detection. Discover how to create FastRP graph embeddings, visualize graph data using Neo4j Bloom, and integrate graph embeddings into machine learning models. Compare SQL and graph database approaches, highlighting scenarios where graph querying proves more efficient. Perfect your graph database skills through hands-on examples and practical applications in data science 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).