Network Analysis with Python: Graph Modeling and NetworkX Basics

In our interconnected world, understanding the relationships between data points is just as important as the data itself. Whether analyzing social networks, transportation routes, or communication systems, graph theory provides the perfect framework to solve these complex relational problems. This text-based course guides you through the fundamentals of network science using Python. You will progress from defining basic graph elements to building and analyzing complex network models, gaining the practical skills to extract meaningful insights from relational data using modern Python libraries and clean coding standards. What you'll learn: - Understand core graph theory concepts, including nodes, edges, directed graphs, and weighted networks. - Create and manipulate network structures programmatically using Python and NetworkX. - Calculate key network metrics such as degree centrality, betweenness, and clustering coefficients. - Integrate network data smoothly with modern pandas DataFrames for streamlined data analysis. - Apply pathfinding algorithms to find shortest paths and analyze network connectivity. - Practice writing clean, modern Python code with type hints to build reliable network models. You will start with foundational definitions and terminology before moving on to practical coding exercises. Through clear written explanations and structured code walk-throughs, you will learn how to load, analyze, and interpret real-world network data at your own pace. This course is designed for beginners and requires only basic Python knowledge; no prior background in graph theory or advanced mathematics is needed. Start exploring the power of network science today.