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

Kód kurzu: VYNA01

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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.
720 EUR

864 EUR s DPH

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Termíny kurzov

Počiatočný dátum: Na vyžiadanie

Forma: E-learning

Dĺžka kurzu: 7 hodín

Jazyk: en

Cena bez DPH: 720 EUR

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Počiatočný dátum: Na vyžiadanie

Forma: Na vyžiadanie

Dĺžka kurzu: 14 hodín

Jazyk: en

Cena bez DPH: 1 200 EUR

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Počiatočný
dátum		 Miesto
konania		 Forma Dĺžka
kurzu		 Jazyk Cena bez DPH
Na vyžiadanie E-learning 7 hodín en 720 EUR Registrovať
Na vyžiadanie Na vyžiadanie 14 hodín en 1 200 EUR Registrovať
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Kontakt

Cieľová skupina

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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.

Štruktúra kurzu

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Concepts in Network Analysis

  • Introduction.
  • Network-level concepts.
  • Adjacency matrices and degree centrality.
  • Introduction to the NETWORK procedure.

Centrality Measures

  • Introduction.
  • Eigenvector centrality.
  • Betweenness and closeness centrality.
  • Influence centrality (self-study).
  • Hub and authority centrality.
  • PageRank centrality.

Analysis of Subnetworks

  • Connected and biconnected components.
  • Maximal cliques.
  • Community detection.
  • Paths, shortest paths, and cycles.
  • Pattern matching.

Bipartite Networks

  • Introduction to bipartite networks.
  • Network projection.

Network Optimization

  • Introduction.
  • 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 Procedure

  • Total unduplicated reach and frequency (TURF) analysis.
  • Multiple traveling salesman problem (mTSP).
  • Minimum cost network flow.

Appendix B: Centrality Measures Using the IML Action Set

  • Introduction.
  • Eigenvector centrality using IML.
  • Hub and authority centrality using IML.
  • PageRank centrality using IML.

Predpokladané znalosti

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In order to complete practices with classroom software, attendees should have basic familiarity with statistics and mathematical concepts and be comfortable programming in SAS using DATA steps. Experience using macros is helpful, but not required.

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+420 731 175 867

edu@edutrainings.cz