Oreilly – Graph Algorithms for Data Science 2024-4

Graph Algorithms for Data Science course. Graphs are a natural way to display and understand related data. This course covers the most important graph-related algorithms and techniques in data science and provides practical advice on their implementation and application. Even if you have no previous experience with graphs, you can benefit from this valuable guide. These powerful algorithms are explained using simple, jargon-free text and images, making it easy to apply them to your own projects.

What you will learn:

• Graph modeling with labeled features
• Create graphs from structured data such as CSV or SQL
• Natural language processing (NLP) techniques for constructing graphs from unstructured data
• Cypher query language syntax for data manipulation and insight extraction
• Social network analysis algorithms such as PageRank and community detection
• How to convert the graph structure into the input of the machine learning model using node embedding models
• Using graph features in node classification and link prediction tasks

This course is a practical guide to working with graph-based data in applications such as machine learning, fraud detection, and business data analysis. The course is full of interesting and fun projects that teach you the alphabet of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and more.

This course is suitable for people who:

• Have basic knowledge about machine learning.
• Be familiar with the Cypher query language described in this course (optional).