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

Course code: VYNA01

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

871 EUR including VAT

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 7 hours

Language: en

Price without VAT: 720 EUR

Starting date: Upon request

Type: Upon request

Course duration: 14 hours

Language: en

Price without VAT: 1 200 EUR

	Starting date	Type	Course duration	Language	Price without VAT
	Upon request	E-learning	7 hours	en	720 EUR	Register

	Upon request	Upon request	14 hours	en	1 200 EUR	Register
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Target group

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.

Course structure

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.

Prerequisites

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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