Detail kurzu

Network Analysis and Network Optimization in SAS(R) Viya(R)

EDU Trainings s.r.o.

Popis kurzu

This course provides a set of network analysis (graph theory) and network optimization solutions using the NETWORK and OPTNETWORK procedures in SAS Viya. Real-world applications are emphasized for each algorithm introduced in this course, including using network analysis as a stand-alone unsupervised learning technique, as well as incorporating network analysis and optimization to augment supervised learning techniques to improve machine learning model performance through input/feature creation. The self-study e-learning includes:Annotatable course notes in PDF format. Virtual lab time to practice.

Obsah kurzu

Concepts in Network AnalysisIntroduction.Network-level concepts.Adjacency matrices and degree centrality.Introduction to the NETWORK procedure.Centrality MeasuresIntroduction.Eigenvector centrality.Betweenness and closeness centrality.Influence centrality (self-study).Hub and authority centrality.PageRank centrality.Analysis of SubnetworksConnected and biconnected components.Maximal cliques.Community detection.Paths, shortest paths, and cycles.Pattern matching.Bipartite NetworksIntroduction to bipartite networks.Network projection.Network OptimizationIntroduction.Linear assignment problem.Minimum spanning tree.Maximum spanning tree (self-study).Traveling salesman problem.Minimum cost network flow (self-study).Appendix A: Network Optimization Using the OPTMODEL ProcedureTotal unduplicated reach and frequency (TURF) analysis.Multiple traveling salesman problem (mTSP).Minimum cost network flow.Appendix B: Centrality Measures Using the IML Action SetIntroduction.Eigenvector centrality using IML.Hub and authority centrality using IML.PageRank centrality using IML.

Cílová skupina

Anyone interested in learning to incorporate network analysis and network optimization to provide solutions and solve real-world business challenges, including data scientists, business analysts, statisticians, and other quantitative professionals. Managers, directors, and leaders with a quantitative background are also encouraged to attend to learn how network analysis and optimization can be integrated into a broader portfolio of data science and machine learning applications.
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