Semantic graph databases can change the way that we look at data, and graph analytics yields new insights into existing and soon-to-be collected datasets. This course will address how graph analytics are used to deal with issues of data quality and data completeness, the implications for the confidence in the conclusions drawn from these analyses, and where the challenges still lie in data migration and data quality. Issues related to node-typed, edge-typed, and directed graphs, using the resource description framework (RDF) to describe information in a graph, using SPARQL, and application of inferential rules and ontologies to the dataset will be discussed.