A graph is the underlying data structure behind social networks, maps, routing networks and logistics, and a whole range of applications that you commonly use today. In this course, Working with Graph Algorithms in Python, you’ll learn different kinds of graphs, their use cases, and how they’re represented in code. First, you’ll dive into understanding the pros and cons of adjacency matrices, adjacency lists, adjacency sets, and know when you would choose one data structure over another. Next, you’ll explore common graph algorithms, such as the topological sort, used to model dependencies in tasks, build components, and manage projects. Additionally, you’ll cover how to find the shortest path in a graph, the core algorithm for mapping technologies. Lastly, you’ll be introduced to spanning tree algorithms, which are used to find a path and covers all nodes with minimum cost, the fundamental algorithm behind figuring flight paths, and bus routes. By the end of this course, you’ll have a better understanding of these principles and the necessary skills to implement them into simple, easy to follow Python code.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more

Table of Contents

• Course Overview
  1min
• Introducing the Graph Data Structure
  50mins
• Understanding Topological Sort
  19mins
• Working with Shortest Path Algorithms
  37mins
• Working with Spanning Tree Algorithms
  25mins