Final Exam: Graph Analytics
Apache Spark | Intermediate
- 1 video | 32s
- Includes Assessment
- Earns a Badge
Final Exam: Graph Analytics will test your knowledge and application of the topics presented throughout the Skillsoft Aspire Graph Analytics Journey.
WHAT YOU WILL LEARN
Use graph nodes and edges to model entities and relationshipsemploy graph nodes and edges to model entities and relationshipsdescribe the use graph nodes and edges to model entities and relationships in the real worldRecall the attributes of the property graph model used to represent knowledge graphsModel graphs using an adjacency list and adjacency set and compare the two representations; Represent graphs using an adjacency list in Python; Represent Graphs using an adjacency set in PythonRepresent Graphs using an adjacency set in PythonRepresent Graphs using an adjacency matrix in Pythonrecall how depth-first and breadth-first traversal works; implement breadth-first traversal using a queue data structure; implement depth-first traversal using a stack, as well as using recursioncompute the shortest path in a weighted graph using greedy traversal and the distance tablerecall the properties of greedy algorithmsdescribe the structure and components of a graph recognize the different types of graphs based on the relationships between the nodesdescribe the properties and features of the Neo4j graph databaseset up Neo4j Desktop on your machine; create a database management system from a dump file in Neo4j Desktop; recognize the features including supporting apps which are available when using Neo4j Desktopuse the Neo4j Browser to run simple queries using the Cypher query languagerecognize how data can be grouped in projects, database management systems, and databasesuse the Cypher shell to create and manage databases in a DBMS; create query parameters and execute Cypher queries from the Cypher shellenable and disable HTTP communication with a Neo4j DBMS and configure the communication portsuse the Neo4j browser to create a new user and assign a built-in role to itrecognize how frequently-run queries can be saved and organized from the Neo4j browserdescribe the use cases as well as the basic syntax of the Cypher query languageprovision nodes with labels as well as properties using the CREATE clause in a Cypher querydefine relationships which have their own properties using the Cypher languageremove unwanted nodes and relationships in a Neo4j grapha variety of MATCH and OPTIONAL MATCH operations when searching for patternsrecognize the use cases of the MERGE clause of a Cypher queryuse the Cypher query language to look for 2nd degree and higher degree connections between two nodes in a Neo4j databaseperform union and intersect operations on data in a Neo4j database using the Cypher query languagesort the results of a query execution using the ORDER BY clausedemonstrates searching for specific nodes in a database using the Bloom search barconfigure the appearance of nodes and relationships in a Neo4j Bloom scene
describe the various data views available in the Bloom user interface, such as the hierarchical and the presentation viewsuse the Neo4j Bloom interface to analyze the nodes in your graphs, including the connections between themrecognize the similarities and differences of data modeling approaches for relational, document and graph datause labels and properties for Neo4j nodes in an optimal manner from the point of view of anticipated queriesdescribe how data in a tabular structure containing many-to-one relationships can be modelled as a Neo4j graphmap the tables in a relational database to a graph structure using the Neo4j ETL toolredefine the nodes and relationships in your Neo4j database using the APOC librarymigrating to Aura with a dump file or using push-to-cloudwrite a Python application to modify and read from the contents of an Aura databaseconnect to an Aura database using the Cypher shell and run queries against itinstall the Graph Data Science library for a Neo4j DBMSCreate an in-memory graph using the native projection configuration for nodes and relationshipsLoad properties from a source database to an in-memory graphbuild a sub-graph containing a subset of elements from an already existing graphadd properties to an in-memory graph based on the computation of an algorithmload properties from the source database of a graph when exporting it to a new databaseexport in-memory graphs to a set of CSV files containing data for nodes and relationshipscreate nodes and relationships from the contents of CSV filesfind individual nodes or clusters of nodes in a network which are not connected to one anothercreate a graph where each relationship has an attached weightperform a breadth-first and depth-first traversal of a graphoutline Apache Hadoop and its ecosystem, describe GraphFrames and their capabilities, and recognize where GraphFrames fit into the Apache Hadoop ecosystemdemonstrate the identification of the most and the least-connected nodes in a graphsearch for patterns of relationships between the nodes in a Spark GraphFrameuse the breadth-first search and the shortestPaths functions to find the shortest paths between nodes in a graphdescribe the different operations performed by individual neurons in a layer of a neural networkset up the Python libraries required to use the Spektral library for building a graph neural network (GNN)outline graph convolutional networks (GCNs) and recognize the operations performed on input data when using a GCN, including symmetric normalizationrecognize the structure required to feed graph data into a graph convolutional network (GCN) modelidentify various factors which can influence the quality of predictions made by a GCN model
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