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advancedInterview PrepPAID

Graph Theory for Technical Interviews

Graphs are everywhere in social networks, recommendation engines, and logistics. This course teaches you how to model real-world problems as graphs and solve them using BFS, DFS, Topological Sort, and Dijkstra's algorithm. Based on the NeetCode and Grokking patterns, you will master the most common graph challenges seen in FAANG interviews.

4.90/5.0
10 hours
0 enrolled
Updated Apr 2026
Course Content ↓
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By CloudaQube Engineering

What You'll Learn

Model complex problems as Adjacency Lists and Matrices in Python
Master Breadth-First Search (BFS) for finding shortest paths in unweighted graphs
Apply Depth-First Search (DFS) for connectivity and cycle detection
Implement Topological Sort for dependency resolution (e.g., Course Schedule)
Solve weighted shortest path problems using Dijkstra's algorithm and Priority Queues

Prerequisites

  • Strong understanding of recursion and queue/stack data structures
  • Familiarity with Python dictionaries and sets

About the Instructor

C

CloudaQube Engineering

Expert instructor with hands-on industry experience in Interview Prep.

Included in paid plans

LevelAdvanced
Duration10 hours
Lessons—
Students0
Rating4.90 / 5.0

This course includes

  • Hands-on practice labs
  • AI-powered explanations
  • Progress tracking
  • Certificate of completion
  • Lifetime access
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