Graph Analytics

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Graph Analytics

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Graph Analytics. 

Graph Analytics journey is ontworpen om data professionals vaardig te maken in de nieuwste grafiektechnologieën. Het geeft een uitgebreid beeld van hoe gegevens die worden weergegeven in de vorm van grafieken bedrijven helpen complexe en dynamische relaties in sterk verbonden gegevens te benutten om inzichten en concurrentievoordeel te genereren. Deze reis begint met de focus op grafiekdatastructuren en algoritmen. Vervolgens wordt een van de populairste grafiekdatabases Neo4j en de Cypher querytaal voor het bevragen van grafiekgegevens geïntroduceerd. Deze reis behandelt ook hoe grafiekanalyse uit te voeren met Neo4j, grafiekvisualisaties en grafiekmodellering in de diept…

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Graph Analytics. 

Graph Analytics journey is ontworpen om data professionals vaardig te maken in de nieuwste grafiektechnologieën. Het geeft een uitgebreid beeld van hoe gegevens die worden weergegeven in de vorm van grafieken bedrijven helpen complexe en dynamische relaties in sterk verbonden gegevens te benutten om inzichten en concurrentievoordeel te genereren. Deze reis begint met de focus op grafiekdatastructuren en algoritmen. Vervolgens wordt een van de populairste grafiekdatabases Neo4j en de Cypher querytaal voor het bevragen van grafiekgegevens geïntroduceerd. Deze reis behandelt ook hoe grafiekanalyse uit te voeren met Neo4j, grafiekvisualisaties en grafiekmodellering in de diepte. Het richt zich ook op grafiek data science met Neo4j en grafiek modellering met Apache Spark. Leerlingen zullen ook begrijpen grafiek neurale netwerken die het meest worden gebruikt in diverse AI-onderzoeksprojecten en deep learning toepassingen.

Deze LearningKit met meer dan 26 uur leren is opgedeeld in drie tracks:

Cursusinhoud

Track 1: Getting Started with Graph

In this track, the focus will be on graph data structures and algorithms.
Courses (4 hours +):

Graph Data Structures: Understanding Graphs & Knowledge Graphs
Course: 1 Hour, 43 Minutes

  • Course Overview
  • Graphs to Model Entities and Relationships
  • The Property Graph Model
  • Plain Old Graphs and Knowledge Graphs
  • Taxonomies and Ontologies
  • Types of Graphs
  • Creating and Visualizing an Undirected Graph
  • Performing Operations on Undirected Graphs
  • Performing Operations on Directed Graphs
  • Executing Graph Algorithms I
  • Executing Graph Algorithms II
  • Course Summary

Graph Data Structures: Representing Graphs Using Matrices, Lists, & Sets
Course: 53 Minutes

  • Course Overview
  • Adjacency Matrix
  • Representing Graphs Using an Adjacency Matrix
  • Representing Other Graph Types Using an Adjacency Matrix
  • Adjacency List and Adjacency Set
  • Representing Graphs Using an Adjacency List
  • Representing Graphs Using an Adjacency Set
  • Course Summary

Graph Data Structures: Implementing Graph Traversal & Shortest Path Algorithms
Course: 1 Hour, 30 Minutes

  • Course Overview
  • Depth-first and Breadth-first Traversal
  • Implementing Breadth-first Traversal
  • Implementing Depth-first Traversal
  • Shortest Path Algorithm
  • Implementing the Shortest Path Algorithm
  • Greedy Algorithms
  • Graph Relaxation Techniques
  • Dijkstra's Algorithm
  • Implementing Dijkstra's Algorithm
  • Course Summary

Track 2: Graph Analytics with Neo4j

In this track, the focus will be on one of the most popular graph databases Neo4j and Cypher query language for querying graph data. This track also covers how to perform graph analytics with Neo4j, graph visualizations and graph modeling in-depth.
Courses (12 hours +)

Graph Analytics with Neo4j: An Introduction to Graph Databases & Neo4j
Course: 1 Hour, 47 Minutes

  • Course Overview
  • An Overview of Graphs
  • Types of Graphs
  • Graph and Relational Structures
  • Graph Databases and Neo4j
  • Downloading and Installing Neo4j Desktop
  • Starting and Stopping a DBMS
  • Exploring the Neo4j Desktop Menu
  • Viewing the Contents of a Graph Database
  • Executing Simple Cypher Queries
  • Querying Based on Relationships
  • Connecting to a Remote Database
  • The help Command
  • Course Summary

Graph Analytics with Neo4j: Administering a Neo4j Database
Course: 56 Minutes

  • Course Overview
  • Creating Neo4j Projects
  • Configuring a Neo4j DBMS
  • Database Administration from the Cypher Shell
  • Running Cypher Queries from the Cypher Shell
  • Modifying DBMS Settings
  • Changing the HTTP Configurations
  • Course Summary

Graph Analytics with Neo4j: Managing Databases with the Neo4j Browser
Course: 53 Minutes

  • Course Overview
  • Creating a New User from the Browser
  • Saving Frequently-used Queries
  • Configuring Query Results
  • Testing the Neo4j HTTP API
  • Course Summary

Cypher Query Language: Creating Nodes & Relationships with Cypher
Course: 1 Hour, 13 Minutes

  • Course Overview
  • An Overview of the Cypher Query Language
  • Downloading and Installing Neo4j Desktop
  • Creating Empty Nodes
  • Creating Nodes with Properties and Labels
  • Defining Relationships Between Nodes
  • Defining Relationships with Properties
  • Course Summary

Cypher Query Language: Basic Reads & Writes with Cypher
Course: 1 Hour, 5 Minutes

  • Course Overview
  • Creating Multiple Nodes and Relationships
  • Matching Relationship Patterns
  • Exploring MATCH Operations
  • Updating Nodes with SET and REMOVE
  • Avoiding Duplication with MERGE
  • Deleting Nodes and Relationships
  • Course Summary

Cypher Query Language: Advanced Operations with Cypher
Course: 1 Hour, 22 Minutes

  • Course Overview
  • Creating Relationships with MERGE
  • Searching for Indirect Connections Between Nodes
  • Finding the Shortest Path Between Nodes
  • Filtering with Logical and String Operations
  • Performing Set Operations
  • Running Aggregation Operations
  • Ordering and Paginating Query Results
  • Course Summary

Working with Neo4j Bloom: Analyzing Graphs
Course: 1 Hour, 35 Minutes

  • Course Overview
  • Setting up a Neo4j Database
  • Loading Graph Data for Analysis
  • Searching for Nodes Using Neo4j Bloom
  • Exploring the Neo4j Bloom User Interface
  • Running Basic Search Queries on Neo4j Bloom
  • Analyzing Scenes from the Card List in Neo4j Bloom
  • Styling the Nodes in a Graph on Neo4j Bloom
  • Editing Data Using Neo4j Bloom
  • Finding Connections between Nodes Using Neo4j Bloom
  • Course Summary

Graph Modeling with Neo4j: An Introduction to Modeling Graphs
Course: 1 Hour, 13 Minutes

  • Course Overview
  • An Overview of Data Modeling
  • Model Data for Neo4j Graph Databases
  • Translating Relational Data to Graphs
  • Defining Nodes for a Graph DB
  • Setting up Relationships Between Nodes
  • Querying a Graph
  • Modeling CSV Data as a Graph
  • Modeling Many-to-one Relationships
  • Course Summary

Graph Modeling with Neo4j: Automating & Refactoring Graph Models
Course: 56 Minutes

  • Course Overview
  • Setting up a Relational Database
  • Importing Data with the Neo4j ETL Tool
  • Verifying the Migration to Neo4j
  • Loading Data from a CSV File
  • Refactoring a Neo4j Schema
  • Course Summary

Database-as-a-Service with Neo4j: The AuraDB Cloud Database Service
Course: 59 Minutes

  • Course Overview
  • Provisioning an Instance of Neo4j Aura
  • Migrating to Aura with a Dump File
  • Migrating to Aura Using Push-To-Cloud
  • Using Neo4j Bloom with Aura
  • Connecting to Aura from Python
  • Interacting with Aura Using Cypher Shell
  • Course Summary

Track 3: Graph Data Science with Neo4j

In this track, the focus will be on building and managing graphs with Neo4j's graph data science library and applying graph algorithms.
Courses (4 hours +)

Neo4j: Building Graphs with Neo4j's Graph Data Science Library
Course: 1 Hour, 46 Minutes

  • Course Overview
  • An Overview of Graph Algorithms
  • Using the Graph Data Science Library
  • Setting Up the Graph Data Science Library
  • Creating a Graph with Native Projection
  • Applying a Page Rank Algorithm on a Graph
  • Projecting Properties in a Graph
  • Accessing Properties from a Graph
  • Creating Cypher Projection Graphs
  • Building Cypher Graphs with Properties
  • Building Subgraphs
  • Course Summary

Neo4j: Managing Graphs with the Graph Data Science Library
Course: 1 Hour, 5 Minutes

  • Course Overview
  • Mutating a Graph
  • Adding a Degree Centrality Score to a Graph
  • Writing Algorithm Results to a Database
  • Exporting a Graph to a Database
  • Exporting Graphs with Additional Properties
  • Deleting Graphs
  • Exporting Graphs to CSV Files
  • Course Summary

Neo4j: Applying Graph Algorithms on In-memory Graphs
Course: 1 Hour, 55 Minutes

  • Course Overview
  • Loading Data from CSV Files
  • Exploring Measures of Centrality
  • Detecting Communities in a Graph
  • Identifying Disconnected Components
  • Applying Page Rank and Article Rank
  • Building a Weighted Graph
  • Using Dijkstra's Shortest Path Algorithm
  • Finding Multiple Shortest Paths
  • Performing Graph Traversal
  • Transforming Nodes to Vectors with FastRP
  • Course Summary

Track 4: Graph Modeling with Apache Spark

In this track, the focus will be on graph modeling with Apache Spark. Learners will also understand graph neural networks which are most widely used in various AI research projects and deep learning applications.
Courses (4 hours +)

Graph Modeling on Apache Spark: Working with Apache Spark GraphFrames
Course: 1 Hour, 52 Minutes

  • Course Overview
  • An Overview of GraphFrames
  • Setting up PySpark and GraphFrames
  • Constructing a GraphFrame
  • Visualizing a GraphFrame
  • Calculating the Degrees of Nodes in a Graph
  • Filtering the Nodes in a GraphFrame
  • Filtering the Edges in a GraphFrame
  • Finding Simple Motifs in GraphFrames
  • Searching for Complex Patterns in Graphs
  • Finding the Shortest Paths between Nodes in a Graph
  • Applying the PageRank Algorithm
  • Course Summary

GNNs: An Introduction to Graph Neural Networks
Course: 1 Hour, 22 Minutes

  • Course Overview
  • The Need for Machine Learning (ML) in Graphs
  • Graph Neural Networks (GNNs)
  • A Neuron's Mathematical Operation
  • Graph Convolutional Networks (GCNs)
  • Knowledge Graphs - A GNN Use Case
  • Installing Modules for Graph Neural Networks (GNNs)
  • Creating Graph Structures Using Spektral
  • Defining a Convolution Function for a GNN
  • Building a Normalized Adjacency Matrix
  • Using a Nonlinear Function in a Convolution
  • Course Summary

GNNs: Classifying Graph Nodes with the Spektral Library
Course: 43 Minutes

  • Course Overview
  • Preparing a Dataset for Classification
  • Building a GCN Classification Model
  • Training and Evaluating a GCN Model
  • Adding Complexity to a GCN Model
  • Course Summary

Final Assessment:
• Final Exam: Graph Analytics will test your knowledge and application of the topics presented throughout the Graph Analytics learning.
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