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Graph Data Science With Python & Neo4j

January 8, 2024 4:41 am

Graph Data Science With Python & Neo4j

Dive into the world of connected data with this practical graph data science Python course. Learn how to use Python and Neo4j to uncover patterns, optimize queries, and solve complex problems using graph theory. Whether you’re working in machine learning, recommendation systems, fraud detection, or social network analysis, this course will teach you how to apply graph-based techniques effectively.

What You’ll Learn

Core concepts of graph theory and graph databases
Introduction to Neo4j and Cypher query language
Building and visualizing graph structures using Python
Applying graph algorithms such as PageRank, centrality, and community detection
Integrating Neo4j with Python libraries (e.g., Pandas, NetworkX, scikit-learn)
Using graph embeddings and link prediction in machine learning
Real-world applications in finance, logistics, and social analysis

Requirements

Basic knowledge of Python programming
Familiarity with data science and machine learning concepts
No prior experience with Neo4j or graph theory required

Course Description

This graph data science Python course is your gateway to mastering graph analytics and modeling. Graphs offer a unique way to represent relationships and dependencies, and are rapidly gaining traction in data science for uncovering insights that traditional models miss.

Starting with the fundamentals, you’ll learn how to represent, query, and analyze graph data using Neo4j’s powerful features. You’ll then dive into practical Python-based graph analysis and machine learning, applying graph algorithms and integrating tools like NetworkX and Neo4j Python drivers. Whether you’re building recommendation engines or mapping out logistics networks, this course empowers you to take full advantage of graph data.

By the end, you’ll be equipped to build, analyze, and deploy graph-powered applications, combining the flexibility of Python with the speed of Neo4j.

About the Instructor

This course is created by experienced data scientists and engineers with expertise in Python, Neo4j, and applied graph theory. Each module combines theory with hands-on coding exercises and real-world datasets to ensure you get practical experience.