Graph Data Structures: Implementing Graph Traversal & Shortest Path Algorithms

Python 3.7    |    Intermediate
  • 11 videos | 1h 29m 48s
  • Includes Assessment
  • Earns a Badge
What makes the graph data structure very interesting and powerful is the large number of algorithms that can be run on graphs to extract insights. Common graph algorithms include traversing a graph and computing the shortest path between nodes. Implementing these algorithms is a great way to learn how graphs are explored and optimized. In this course, learn how graphs can be traversed by studying both depth-first and breadth-first graph traversal and discover how they can be implemented using a stack and a queue respectively. Next, explore how to compute the shortest path in an unweighted graph. And finally, use Dijkstra's algorithm to compute the shortest path in a weighted graph. Upon completion of this course, you will be able to implement optimal algorithms on graphs.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recall how depth-first and breadth-first traversal works
    implement breadth-first traversal using a queue data structure
    implement depth-first traversal using a stack, as well as using recursion
    compute the shortest path in an unweighted graph using breadth-first traversal and the distance table
    implement the shortest path algorithm for an unweighted graph
  • recall the properties of greedy algorithms
    list the relaxation techniques used to populate the distance table in Dijkstra's algorithm
    compute the shortest path in a weighted graph using greedy traversal and the distance table
    implement Dijkstra's algorithm to compute the shortest path in a weighted graph
    summarize the key concepts covered in this course

IN THIS COURSE

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