Basic algorithms on tree data structures, binary search trees, self-balancing trees, graph data structures and basic traversal algorithms on graphs. This course also covers advanced topics such as kd-trees for spatial data and algorithms for spatial data.



Trees and Graphs: Basics
This course is part of Foundations of Data Structures and Algorithms Specialization

Instructor: Sriram Sankaranarayanan
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(132 reviews)
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What you'll learn
Define basic tree data structures and identify algorithmic functions associated with them
Execute traversals and create graphs within a binary search tree structure
Describe strongly connected components in graphs
Skills you'll gain
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There are 4 modules in this course
In this module, you will learn about binary search trees and basic algorithms on binary search trees. We will also become familiar with the problem of balancing in binary search trees and study some solutions for balanced binary search trees such as Red-Black Trees.
What's included
5 videos11 readings5 assignments1 programming assignment1 discussion prompt
In this module, you will learn about graphs and various basic algorithms on graphs such as depth first/breadth first traversals, finding strongly connected components, and topological sorting.
What's included
7 videos6 readings4 assignments1 programming assignment
Union Find Data-structure with rank compression. Spanning trees and properties of spanning trees. Prim’s algorithm for finding minimal spanning trees. Kruskal’s algorithm for finding minimal spanning trees.
What's included
5 videos5 readings4 assignments1 programming assignment
In this module, you will learn about: Shortest Path Problem: Basics. Bellman-Ford Algorithm for single source shortest path. Dijkstra’s algorithm. Algorithms for all-pairs shortest path problem (Floyd-Warshall Algorithm)
What's included
6 videos6 readings3 assignments1 programming assignment
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Reviewed on Aug 20, 2024
The CLRS version for University of Colorado students to access online has some Chapter number changes leading to confusion about what chapter to read in weeks 3 and 4 of this course.
Reviewed on May 2, 2023
This course is definitely hard but for some good reason. It stretches the mind of the learner/student to achieve algorithmic nirvana.
Reviewed on Aug 27, 2023
Some typos are not acknowledged. The evaluations are disconnected from the lectures.
Recommended if you're interested in Computer Science
University of Colorado Boulder
University of Colorado Boulder
University of Colorado Boulder


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