What will you learn in Mastering Graph Algorithms Course
Model real-world problems as graphs and understand core graph representations (adjacency lists, matrices)
Traverse graphs using BFS and DFS, and apply these for connectivity, cycle detection, and topological sorting
Compute shortest paths with Dijkstra’s, Bellman–Ford, and A* algorithms, including handling negative weights
Build minimum spanning trees via Kruskal’s and Prim’s algorithms for network design and clustering
Solve advanced flow problems: Ford–Fulkerson, Edmonds–Karp, and maximum bipartite matching
Program Overview
Module 1: Graph Fundamentals & Representations
⏳ 1 hour
Topics: Definitions, directed vs. undirected, weighted vs. unweighted, adjacency structures
Hands-on: Implement and compare adjacency list and matrix representations
Module 2: Breadth-First & Depth-First Search
⏳ 1.5 hours
Topics: BFS for shortest unweighted paths, DFS for connectivity, cycle detection, and backtracking
Hands-on: Code BFS/DFS routines; apply DFS to find connected components and topological sort
Module 3: Shortest Path Algorithms
⏳ 2 hours
Topics: Dijkstra’s algorithm with priority queues, Bellman–Ford for negative edges, A* heuristics
Hands-on: Implement each algorithm; compare performance on sample road-network data
Module 4: Minimum Spanning Trees
⏳ 1.5 hours
Topics: Greedy strategies, Kruskal’s with Union-Find, Prim’s with heaps
Hands-on: Build MSTs for weighted graphs and visualize resulting tree structures
Module 5: Network Flow & Matching
⏳ 2 hours
Topics: Max-flow/min-cut theorem, Ford–Fulkerson, Edmonds–Karp, bipartite matching via flow reduction
Hands-on: Solve flow problems on capacity graphs and implement bipartite matching
Module 6: Advanced Topics & Applications
⏳ 1.5 hours
Topics: Graph coloring, strongly connected components (Kosaraju’s/Tarjan’s), planarity and embeddings
Hands-on: Detect SCCs in directed graphs; apply graph coloring to scheduling problems
Module 7: Real-World Case Studies
⏳ 1 hour
Topics: Recommendation systems via graph algorithms, influence maximization, route optimization
Hands-on: Prototype a simple friend-recommendation engine and a shortest-route planner
Module 8: Capstone Project – End-to-End Graph Solver
⏳ 2 hours
Topics: Problem selection, algorithm choice, performance tuning, and scalability considerations
Hands-on: Build a full-featured graph-analysis tool that ingests dataset, runs selected algorithms, and visualizes results
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Job Outlook
Algorithm Engineer: $100,000–$150,000/year — design and optimize graph-based solutions for search, recommendation, and AI pipelines
Data Scientist / Machine Learning Engineer: $110,000–$160,000/year — apply graph analytics in network analysis, knowledge graphs, and NLP
Software Engineer (Backend / Infrastructure): $90,000–$140,000/year — implement scalable graph-processing systems in domains such as logistics and social networks
Mastering graph algorithms positions you for roles at top tech companies working on search engines, social platforms, and high-performance computing.
Specification: Mastering Graph Algorithms
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