This course provides a set of network analysis (graph theory) and network optimization solutions using the NETWORK and OPTNETWORK procedures in SAS Viya. Realworld applications are emphasized for each algorithm introduced in this course, including using network analysis as a standalone 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 selfstudy elearning includes:
 Annotatable course notes in PDF format.
 Virtual lab time to practice.
Learn how to
 Structure networks as matrices and in the required data format (or formats) to read network data into the NETWORK and OPTNETWORK procedures.
 Define the fundamental components of network topology, including nodes, links, selflinks, link weights, node weights, and directionality to understand the different ways to construct a network.
 Compute and interpret networklevel measures, including network density, diameter, and average shortest path.
 Compute and interpret centrality measures, including degree centrality, eigenvector centrality, betweenness centrality, closeness centrality, and PageRank centrality.
 Compute, apply, and interpret subnetwork analyses such as connected components, shortest paths, cycles, cliques, and community detection, among others.
 Perform network querying from graph database network structures using the PATTERNMATCH statement.
 Perform network projection to transform a bipartite network into a single network with realworld applications.
 Apply network optimization algorithms such as the linear assignment problem, the traveling salesman problem, and the minimum spanning tree, among others, to solve realworld problems.
Who should attend
Anyone interested in learning to incorporate network analysis and network optimization to provide solutions and solve realworld 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.
Kurzy jsou dostupné jako  Délka   
Live Web: 
4 x 3.5 hodina sessions 
eLearning: 
14 hodin/180 den licence 

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.
This course addresses SAS Viya software.