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+44 (0) 207 874 5000

Graph Databases for Data Analytics and AI

Course 1294

  • Duration: 3 days
  • Labs: Yes
  • Language: English
  • Level: Intermediate

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 Training Delivery Methods

  • In-Person

  • Online

Graph Databases for Data Analytics and AI Training Information

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 specialised 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

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

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

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

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

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

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

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
Get This Course £1,780 For Team Prices Call: 44 (0) 207 874 5000

  • 4-day instructor-led training course

  • Hands-on labs included

  • After-course sandbox included

  • Learning Tree exam included to test your skills

  • After-course coaching available

Get the Advantage £3,150
  • Take this course and gain unlimited access to more than 300 virtual instructor-led courses
  • Future-proof your career with more than 100 sought-after certifications in the market
  • Build real skills through hands-on learning in more than 180 virtual labs
  • Grow your skills and capabilities with more than one course at a time and save
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  • Nov 13 - 15 2:00 PM - 9:30 PM GMT
    Virtual
  • Dec 4 - 6 9:00 AM - 4:30 PM GMT
    London or Virtual
  • Dec 11 - 13 2:00 PM - 9:30 PM GMT
    New York or Virtual
  • Jan 8 - 10 2:00 PM - 9:30 PM GMT
    Ottawa or Virtual
  • Jan 22 - 24 9:00 AM - 4:30 PM GMT
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  • Feb 12 - 14 2:00 PM - 9:30 PM GMT
    Herndon, VA or Virtual
  • Mar 5 - 7 9:00 AM - 4:30 PM GMT
    London or Virtual
  • Mar 12 - 14 1:00 PM - 8:30 PM GMT
    New York or Virtual
  • Apr 9 - 11 2:00 PM - 9:30 PM BST
    Ottawa or Virtual
  • Apr 15 - 17 9:00 AM - 4:30 PM BST
    London or Virtual
  • May 14 - 16 2:00 PM - 9:30 PM BST
    Herndon, VA or Virtual
  • May 28 - 30 9:00 AM - 4:30 PM BST
    London or Virtual
  • Jun 11 - 13 2:00 PM - 9:30 PM BST
    New York or Virtual
  • Jul 16 - 18 2:00 PM - 9:30 PM BST
    Ottawa or Virtual
  • Jul 16 - 18 9:00 AM - 4:30 PM BST
    London or Virtual
  • Aug 13 - 15 2:00 PM - 9:30 PM BST
    Herndon, VA or Virtual
  • Aug 27 - 29 9:00 AM - 4:30 PM BST
    London or Virtual
  • Sep 10 - 12 2:00 PM - 9:30 PM BST
    New York or Virtual

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  • Bring this or any training to your organisation

  • Full-scale programme development

  • Delivered when, where, and how you want it

  • Blended learning models

  • Tailored content

  • Expert team coaching

Get the Advantage £3,150
  • Take this course and gain unlimited access to more than 300 virtual instructor-led courses
  • Future-proof your career with more than 100 sought-after certifications in the market
  • Build real skills through hands-on learning in more than 180 virtual labs
  • Grow your skills and capabilities with more than one course at a time and save
Thank you for choosing subscriptions!
#1294
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Graph Databases for Data Analytics and AI Training FAQs

This course is designed for software developers and database administrators who want to learn about graph databases. It is not designed for IT staff who already have extensive graph database experience.

It is likely that managers and executives will find the hands-on exercises too technical.

Neo4j is the market leader among graph databases, and Neo4j 5.10 is used in many of the course exercises. But the course focus is on the general principles and applications of graph databases; several different graph database platforms are used.

Roughly one-third of the class is devoted to hands-on exercises. The student is also provided with many data files which they can download if they wish to continue their exploration on their own systems or after the end of class.

Absolutely.

Yes, and the rapid rate of growth of graph databases is expected to continue.

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