Udemy – Modern Graph Theory Algorithms with Python 2025-2

Modern Graph Theory Algorithms with Python. In this comprehensive, project-based course, you’ll step into the fascinating world of graph theory and its practical applications. Whether you’re a data scientist, software engineer, or algorithm enthusiast, you’ll learn how to use graph algorithms in Python to solve real-world problems.

What you will learn:

• Mastering fundamental graph theory algorithms: Learn and efficiently implement algorithms such as DFS, BFS, Dijkstra’s algorithm using Python and NetworkX
• Building a social network analyzer: Building a complete social network analyzer from scratch, including visualization tools and community detection algorithms
• Implementation of optimal path finding algorithms: Implementation and optimization of path finding algorithms for real-world applications such as urban navigation systems and transportation networks.
• Design and development of optimal network infrastructures: Design and development of optimal network infrastructures using minimum spanning tree algorithms (Crow Scal and Prim)
• Create professional graph visualizations: Create interactive displays and network analysis tools using NetworkX and Matplotlib
• Application of centrality criteria and PageRank algorithm: Analyzing influence and importance in social networks using centrality criteria and PageRank algorithm
• Recommender Systems Development: Developing recommender systems using graph-based algorithms and machine learning techniques.
• Mastery of advanced network analysis techniques: Mastery of advanced techniques such as community detection, bipartite graphs, and joint points.
• Building four complete real-world projects: demonstrating practical applications of graph theory in modern software development

Who is this course suitable for?

• Python developers who want to expand their skills in graph theory and network analysis, especially those interested in building practical applications
• Data scientists and analysts who want to master network visualization and graph-based algorithms for complex data analysis and machine learning
• Computer science students or self-taught individuals who want to gain practical experience in implementing graph algorithms beyond theoretical classroom knowledge
• Software engineers who work with network systems, social platforms, or recommendation engines and need practical skills implementing graph algorithms
• IT professionals looking to understand network optimization and analytics through modern Python tools and libraries
• Technology professionals who are transitioning into roles such as social media analytics, route optimization, or network infrastructure design

Modern Graph Theory Algorithms with Python course specifications

• Publisher:  Udemy
• Instructor:  Meta Brains , Skool of AI
• Training level: Beginner to advanced
• Training duration: 2 hours and 21 minutes
• Number of lessons: 39

Course headings

Prerequisites for the Modern Graph Theory Algorithms with Python course

• Basic Python programming experience (variables, functions, loops, and basic data structures). No advanced Python knowledge required
• Basic understanding of data structures (arrays, lists, dictionaries). No prior graph theory knowledge needed
• Python 3.x installed on your computer (Windows, Mac, or Linux)
• Familiarity with using pip to install Python packages (we’ll guide you through installing NetworkX and Matplotlib)
• Basic math skills (high school level algebra). No advanced mathematics required
• A computer with minimum 4GB RAM and any modern operating system
• Text editor or IDE of your choice (we recommend VS Code, but any will work)
• Enthusiasm to learn about networks and graph algorithms – perfect for beginners in graph theory!

Course images

Sample course video

Download link

Download Part 1 – 1 GB

Download Part 2 – 89 MB

File(s) password: www.downloadly.ir

File size

1.08 GB