Network Data Representations with Python and NetworkX

Understanding how to represent connection-based data is essential for analyzing modern social networks, transport systems, and web structures. This text-based course guides you through the foundational concepts of graph theory and network representations using Python and the NetworkX library. By reading through detailed explanations and structured code examples, you will transition from a beginner to a confident data practitioner capable of structuring, manipulating, and querying network data. You will learn how to choose the right data representation for any network analysis task to optimize memory and speed. What you'll learn: 1. Understand fundamental graph theory concepts, terminology, and different network types. 2. Create and manipulate graphs using the NetworkX library in Python. 3. Represent networks using edge lists, adjacency lists, and adjacency matrices. 4. Apply modern Python type hints to document and structure your network analysis code. 5. Convert network data between NetworkX structures and modern pandas DataFrames. 6. Analyze network properties like node degree, connectivity, and path lengths through written code walk-throughs. The course begins with essential graph theory definitions and core terminology before moving into practical representations. You will then explore how to load, convert, and manipulate network data using various storage formats and modern Python practices. This course is designed for beginner programmers, data analysts, and software developers who want to understand network structures. No prior experience with graph theory or NetworkX is required, though a basic familiarity with Python is helpful. Start reading today to unlock the power of network-based data analysis.