Graph Databases for Data Analytics and AI

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

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

Commercial Pricing

23 950 kr

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

Would you like to take this course as a team?

Include dates with afternoon start times

jul 8 - 10 15:00 - 22:30 CEST

Herndon, VA or Virtual
jul 15 - 17 9:00 - 16:30 CEST

Stockholm or Virtual
jul 15 - 17 10:00 - 17:30 CEST

London or Virtual
sep 2 - 4 9:00 - 16:30 CEST

Stockholm or Virtual
sep 2 - 4 10:00 - 17:30 CEST

London or Virtual
sep 9 - 11 15:00 - 22:30 CEST

Herndon, VA or Virtual
nov 4 - 6 15:00 - 22:30 CET

Herndon, VA or Virtual
nov 11 - 13 9:00 - 16:30 CET

Stockholm or Virtual
nov 11 - 13 10:00 - 17:30 CET

London or Virtual
jan 6 - 8 15:00 - 22:30 CET

Herndon, VA or Virtual
jan 13 - 15 9:00 - 16:30 CET

Stockholm or Virtual
jan 13 - 15 10:00 - 17:30 CET

London or Virtual
mar 10 - 12 9:00 - 16:30 CET

Stockholm or Virtual
mar 10 - 12 10:00 - 17:30 CET

London or Virtual
mar 10 - 12 15:00 - 22:30 CET

Herndon, VA or Virtual
maj 12 - 14 9:00 - 16:30 CEST

Stockholm or Virtual
maj 12 - 14 10:00 - 17:30 CEST

London or Virtual
maj 12 - 14 15:00 - 22:30 CEST

Herndon, VA or Virtual

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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.

Graph Databases for Data Analytics and AI

Graph Databases for Data Analytics and AI Training Delivery Methods

Graph Databases for Data Analytics and AI Training Information

Graph Databases for Data Analytics and AI Training Outline

Module 1: Why Graph Databases are Important

Module 2: Foundations of Graphs and Graph Data

Module 3: Graph Database Platforms

Module 4: Querying and Analysing Graph Data

Module 5: Using Graph Database APIs to Build Systems

Module 6: Knowledge Graphs

Module 7: Graph Neural Networks

Live Instructor 23 950 kr

Graph Databases for Data Analytics and AI Training FAQs

Who should attend this course?

Can my manager take this course?

Is this a course for Neo4j?

How much time is spent doing hands-on exercises?

Can you get started with graph databases using free and/or open-source software?

Are companies using graph databases for practical applications?

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