Learn Neo4j Database and Graph Algorithms
- 4.5 hours on-demand video
- 1 downloadable resource
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- Understand the science of graph theory, databases, and its advantages over traditional databases
- Install Neo4j and learn the most common practices of traversing data
- Understand the problems while working with nodes and with large graphs of information
- Learn proper skills for data modelling and querying capabilities of graph databases
- Understand why relational databases are replaced by graph databases
- Learn Cypher Query Language that can be used for modifying, creating, and deleting data
- Use Neo4j graph algorithms library with your real data
- Solve routing problems by finding paths inside a connected graph
- Create a group of nodes sharing common properties, aka communities
- Build a recommendation system using similarity measurement between nodes
- No previous graph database experience is required; however, some basic database knowledge will help you understand the concepts more easily.
With increase in complexity of data relationships, graph databases are quickly becoming the de-facto standard for organizations who manage large volumes of connected data. Neo4j is a graph database that allows traversing huge amounts of data with ease. It is the world's leading graph database management system which is designed for optimizing fast management, storage, and traversal of nodes and relationships.
Starting with a brief introduction to graph theory, this course will show you the advantages of using graph databases along with data modelling techniques for graph databases. You will gain practical hands-on experience with commonly used and lesser known features for updating graph store with Neo4j's Cypher query language. You will learn to use it for artificial intelligence, fraud detection, graph-based search, network ops & security, and many other use cases.
Furthermore, you will learn the important graph algorithms which are used in Neo4j’s graph analytics platform wherein you will explore various high-performance graph algorithms that help reveal hidden patterns and structures in your connected data. You will also gain skills to use the algorithms efficiently to understand, model, and predict complicated dynamics.
By the end of this course, you will be confident using graph analytics with Neo4j to effectively handle large volume of connected data and to use its quick insights to wield powerful results.
Meet Your Expert(s):
We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:
Shehzad Ahmed is an enthusiast software engineer having a great grip and hands-on experience with multiple programming languages i.e. C/C++ Java and C#.NET, JS, PHP. He has worked on web and mobile development with various APIs and open source libraries. Currently, he is working as a Magento 2 E-commerce developer at fmeextension. Besides that, he is also a freelancer on Fiverr since the last 2 years and has completed over 200 projects with 4.9/5.0 rating and has worked on Neo4j extensively.
Estelle Scifo has more than 7 years of work experience as a data scientist. As a Neo4j certified professional, she uses graph databases on a daily basis and takes full advantage of its features to build efficient machine learning models out of this data. Besides that, she is also a data science mentor to guide newcomers into the field. The domain expertise and the beginner’s mind make her an excellent teacher.
- This course is for developers, web developers and aspiring data scientists who are new to graph analytics and who wish to use the power of Neo4j for their data in their applications.
In this video, you will look at Graph Database. You will also see the comparison between Relational Database and Graph Database.
Look at Graph Database
Compare Relational Database and Graph Database
In this video, you will start building the first real-world application of graph algorithm- A path finder for public transport.
Load the prepared graph data
Find the shortest path between two stations (simple scenario)
Understand the algorithm details with more subtle examples
The aim of this video is to summarize the techniques that can be used to build a recommendation system, focusing on those that can work on real-time with Neo4j
Learn about some simple recommendation engine techniques
Discover the Northwind dataset
Write queries for recommendation without graph algorithms
The goal of this video is to show how community detection can be used in the context of recommendation
Apply community detection algorithm to group similar products
Use this information in a recommendation system
Learn how to choose the best performing recommendation method
The goal of this video is to summarize what we have learned in this course and show perspectives about the future of graphs and machine learning
Summarize the key points of the course
Get some advices about future learning
Discover a new visualization tool helpful for graph analysis