Fundamentals of Graph Databases: On-Demand

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.

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. 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 o Graph software • Graph data in files o File format alternatives o Scripts for manipulating and characterising graph data • Diagramming graphs o Diagramming graphs with Python o 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 o Structure of Cypher® queries o 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) o Formats for representing RDF triples • Ontologies o The structure of ontology documents o Tools for creating and editing ontologies o Protege and other Tools • Semantic Nets and Knowledge graphs o Loading knowledge graphs into a graph database o 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 o Creating graph neural networks • Applying convolutional neural networks to graph data o Review the basics of convolutional neural networks o Training the hybrid AI

This hands-on Introduction to Artificial Intelligence course will introduce you to the key concepts in modern AI and discuss how they apply to you and your business.

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In this AI Solutions with Azure course, you'll learn how to provision Azure OpenAI service, deploy models, and use them in generative AI applications.

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Join our hands-on 2-day course to build practical skills in NLP and Generative AI training to develop advanced AI models like ChatGPT from OpenAI.

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