Graph Algorithms for Data Science: With Examples in Neo4j

Description

Graph Algorithms for Data Science

Graph Algorithms Data Science is a comprehensive and practical course designed to help learners harness the power of graph-based analysis and machine learning. This course provides hands-on experience with Neo4j, one of the most popular graph databases, enabling you to solve real-world problems in data science, AI, and analytics. Learn how to model, visualize, and analyze complex data relationships to gain valuable insights.

Course Description

Graphs are at the heart of modern data science—whether it’s social networks, recommendation engines, or fraud detection systems. In this course, Graph Algorithms for Data Science: With Examples in Neo4j, you’ll discover how graph algorithms transform raw data into actionable insights. Through step-by-step examples and coding exercises, you’ll learn how to use Neo4j’s Graph Data Science (GDS) library to build efficient, scalable models that reveal hidden connections in your data.

You’ll explore algorithms such as PageRank, Community Detection, Centrality, and Pathfinding—applied to real datasets using Neo4j’s Cypher query language. Whether you’re a data scientist, software engineer, or researcher, this course will empower you to integrate graph analytics into your workflow and make smarter, data-driven decisions.

What You’ll Learn

• Understand the fundamentals of graph theory and graph databases.
• Use Neo4j and Cypher for data modeling and querying.
• Implement popular graph algorithms like PageRank, Louvain, and Betweenness Centrality.
• Integrate graph-based machine learning techniques for predictive analysis.
• Visualize relationships and communities within complex networks.

Requirements

• Basic understanding of Python or any programming language.
• Familiarity with data structures and SQL is recommended.
• A computer with Neo4j Desktop or AuraDB installed.

About the Publication

This course is inspired by real-world data science use cases and industry practices. The author is a seasoned data scientist and Neo4j practitioner with over 10 years of experience in advanced analytics, graph modeling, and AI-driven decision systems. Their work focuses on bridging the gap between data engineering and intelligent systems through graph technologies.

Explore These Valuable Resources

• Neo4j Graph Data Science Library
• Graph Algorithms Explained
• DataCamp Neo4j Tutorial

Explore Related Courses

• Data Science Courses
• Machine Learning Courses
• Artificial Intelligence Courses
• Python Programming Courses
• Database Technologies Courses

Why Take This Course?

This course offers a rare combination of theory and practice. You’ll not only understand how graph algorithms work but also gain the skills to implement them efficiently in real-world data pipelines. Every topic is explained clearly, with visual examples and guided exercises that reinforce your understanding. By the end, you’ll have the confidence to apply graph algorithms to enhance your data-driven solutions and improve analytical outcomes.