Learn about the graph analytics (also known as network analytics) methodology and how it applies to data science, most useful in areas with lots of volume, relationships, and overall data set complexity. Assess the key types of graph analytic approaches including path analysis, centrality analysis, community analysis, and connectivity analysis. Explore the most important graph concepts such as nodes, arcs, walks, trails, circuits, degree, trees, forests, and components. Understand the most useful graph algorithms including Warshall’s Algorithm, Depth-First and Breadth-First Searches, Dijkstra’s Algorithm, Kruskal’s and Prim’s algorithm, the Lightest Hamiltonian Circuit, and PageRank. Explore the many use cases of graph analytics in areas such as logistics, scheduling, clustering, and analyzing social media data.
Here is a link to all of Zacharias Voulgaris’ machine learning, data science, and artificial intelligence (AI) videos.