Modern Graph Theory Algorithms with Python
Harness the power of graph algorithms and real-world network applications using Python
Created by Colleen M. Farrelly, Franck Kalala Mutombo
Explore how graph theory and Python come together to tackle complex data challenges. You'll learn to structure, analyze, and draw insights from massive datasets using practical network science techniques. Real-world examples help you apply these skills to problems in analytics, prediction, and more.
Packt | Jun 2024 | 290 min
What You Will Learn
You will work through practical case studies that show how to convert raw data into network formats and apply graph algorithms step by step. Each topic is paired with hands-on Python code, so you can see how the theory translates into real analysis. By following these examples, you'll build confidence in using network science tools for your own projects.
Key Features
- Transform diverse datasets into network structures for deeper analysis
- Apply efficient graph algorithms to real-world data science problems
- Use Python to build, query, and analyze graph databases and networks
Target Audience
This course is ideal for data analysts, researchers, and professionals with a working knowledge of Python, pandas, and NumPy. If you're looking to expand your toolkit with network science and graph algorithms to solve real-world problems, you'll find practical value here. Prior experience with datasets will help you get the most from the material.
