In this Graph Databases for Data Analytics and AI Training course, you will learn how databases for graph data can support data analytics and artificial intelligence in ways not practical with traditional relational databases. You will see practical and profitable use cases and understand the importance of graph data in today’s world. You will master the practical aspects of importing and exporting data for graph databases, and how graph databases are searched and queried. Graph databases are also ideal for the representation of human knowledge, not just simple facts. You will understand how knowledgebase graphs integrate with artificial intelligence systems and learn to use these systems to provide business analyses and insights. Graph Databases for Data Analytics and AI Benefits In this course, you will: Learn what graph data is and why graph data is critical in today’s business environment. See how businesses and government agencies are using graph data. Explore the differences among the many graph database platforms. Understand the different file formats used to import and export graph data. Query graph databases to obtain specialized data and important insights. Understand the differences among the different query languages for graph data. Learn how human knowledge can be represented using knowledge graphs. Construct a database of knowledge, as opposed to simple facts. Use a “knowledgebase” to infer new insights not present in the stored data. Understand how the techniques of graph data and advanced neural networks can work together for advanced data analytic and artificial intelligence systems. Training Prerequisites Attendees should have a general understanding of the fundamentals of databases, but database experience is not required. Experience with some programming or scripting language is expected, but a detailed knowledge or Python is not required. Graph Databases for Data Analytics and AI Training Outline Module 1: Why Graph Databases are Important Objectives: Understand what graph data is and why it is important Explore a few or the many practical applications of graph databases Contents: What are graphs? How graphs are used to provide structure to data Business use-cases of graph databases Module 2: Foundations of Graphs and Graph Data Objectives: Storing graph data in simple files What is a relational database? How graph databases differ from traditional relational databases Contents: How graphs are used to provide structure to data Graph software Graph data in files File format alternatives Scripts for manipulating and characterising graph data Diagramming graphs Diagramming graphs with Python Using GraphViz software Module 3: Graph Database Platforms Objectives: Examine commercial graph database systems Understand the differences among graph database systems Contents: Options for graph database systems Introduction to Neo4j Introduction to GraphDB Introduction to CogniPy Module 4: Querying and Analysing Graph Data Objectives: Learn how to query data in a graph database Understand the differences among graph database query languages Contents: Using the Neo4j browser The Cypher® graph query language Structure of Cypher® queries Viewing and interpreting query results Other graph query languages GraphQL Gremlin SPARQL Module 5: Using Graph Database APIs to Build Systems Objectives: Understand how APIs are used to build a system Explore some examples using Python Contents: Using Python to manage and query a Neo4j graph database How graphs are used to provide structure to data Module 6: Knowledge Graphs Objectives: Learn how human knowledge can be represented in graph data using semantic nets Create a simple knowledge graph Understand ontologies Contents: Semantic nets and the Resource Description Framework (RDF) Formats for representing RDF triples Ontologies The structure of ontology documents Tools for creating and editing ontologies Protege and other Tools Semantic Nets and Knowledge graphs Loading knowledge graphs into a graph database Applying logic-based “reasoners” to knowledge graphs Module 7: Graph Neural Networks Objectives: Understand the Need for “Hybrid” AI Applying convolutional neural networks to graph data Contents: “Hybrid” AI Creating graph neural networks Applying convolutional neural networks to graph data Review the basics of convolutional neural networks Training the hybrid AI
