Uncovering Hidden Relationships
Learn the basics of graph theory before modelling data as a distributed knowledge graph and thereafter undertaking complex network analysis to uncover hidden patterns and relationships in order to derive actionable intelligence from structured and unstructured data.
This course provides a hands-on and in-depth exploration of graph theory applied to complex network analysis in order to uncover hidden patterns and relationships from structured and unstructured disparate datasets. This course explores in both theoretical and applied detail the major techniques used in graph theory and complex network analysis including bipartite graphs, similarity measures, extracting cliques and asymmetric relationships. This course also introduces distributed graph computing frameworks including Apache TinkerPop and the industry-standard Gremlin graph traversal language, along with useful tips for optimising the performance of massive graphs. Using these techniques, experienced senior data scientists and data engineers can model their disparate datasets as a distributed knowledge graph and perform complex network analysis that can be applied to a wide range of exciting use-cases including recommendation systems, fraud and criminal activity detection systems, DNA sequencing, location services, and social network analysis.