Graph Data Structures and Algorithms with Python
About this course
The Graph Data Structures and Algorithms course provides an in-depth understanding of how graphs are used to represent and solve real-world computational problems. Students will explore the fundamental concepts of graphs, including vertices, edges, degrees, adjacency, and connectivity, and will learn to differentiate between various types of graphs such as directed, undirected, weighted, and unweighted. The course emphasizes practical learning through the implementation of key graph algorithms, including traversal techniques (BFS, DFS), shortest path algorithms (Dijkstra’s, Bellman-Ford, Floyd-Warshall), and optimization algorithms (Prim’s and Kruskal’s for Minimum Spanning Trees). Learners will also study topological sorting, connectivity analysis, and real-world applications in network design, data analysis, and cybersecurity. By the end of the course, students will be able to model complex systems using graphs, select appropriate algorithms for given problems, and analyze their performance in terms of time and space complexity.
63/100
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- Who stands behind it
- 8/35
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- 16/20
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What you'll learn
- understand fundamental concepts of graphs
- implement key graph algorithms such as BFS, DFS, Dijkstra’s, Bellman-Ford, Prim’s, and Kruskal’s
- model complex systems using graphs
- analyze algorithm performance in terms of time and space complexity
Course objectives
- to differentiate between various types of graphs
- to apply graph algorithms to real-world problems
- to analyze the efficiency of different algorithms
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