Course Summary
This course introduces the concept of graph analytics and how it is used in big data processing. Students will learn about graph theory, graph databases, and graph processing systems.Key Learning Points
- Understand the basics of graph theory and its applications in big data analytics
- Learn about graph databases and their role in data processing
- Explore graph processing systems and their use in analyzing large datasets
Related Topics for further study
Learning Outcomes
- Understand the basics of graph theory and its applications in big data analytics
- Learn about graph databases and their role in data processing
- Gain hands-on experience with graph processing systems and analytics tools
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming concepts
- Familiarity with data analysis and database management
Course Difficulty Level
IntermediateCourse Format
- Online course
- Self-paced learning
- Video lectures
- Hands-on projects
Similar Courses
- Big Data Essentials: Hadoop, MapReduce, and Spark RDD
- Data Science Essentials
- Big Data Modeling and Management Systems
Related Education Paths
Related Books
Description
Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data.
Outline
- Welcome to Graph Analytics
- Welcome to Graph Analytics for Big Data
- Introduction to Graphs
- What is a Graph?
- Why Graphs?
- Why Graphs? Example 1: Social Networking
- Why Graphs? Example 2: Biological Networks
- Why Graphs? Example 3: Human Information Network Analytics
- Why Graphs? Example 4: Smart Cities
- The Purpose of Analytics
- What are the impact of Big Data's V's on Graphs?
- What to learn in this module
- Download Slides for this Module
- Introduction to Graphs
- Graph Analytics
- Focusing On Graph Analytics Techniques
- Path Analytics
- The Basic Path Analytics Question: What is the Best Path?
- Applying Dijkstra's Algorithm
- Inclusion and Exclusion Constraints
- Connectivity Analytics
- Disconnecting a Graph
- Connectedness: Indegree and Outdegree
- Community Analytics and Local Properties
- Global Property: Modularity
- Centrality Analytics
- Optional Lecture 1: Bi-directional Dijkstra Algorithm
- Optional Lecture 2: Goal-directed Dijkstra Algorithm
- Optional Lecture 3: Power Law Graphs
- Optional Lecture 4: Measuring Graph Evolution
- Optional Lecture 5: Eigenvector Centrality
- Optional Lecture 6: Key Player Problems
- What to learn in this module
- If this module takes a little longer... that's OK!
- Download All Slides for Module 3
- Graph Analytics Applications
- Connectivity, Community, and Centrality Analytics
- Graph Analytics Techniques
- Welcome to Graph Analytics Techniques
- Hands-On: Downloading, Installing, and Running Neo4j
- Hands-On: Getting Started With Neo4j
- Hands-On: Modifying a Graph With Neo4j
- Hands-On: Importing Data Into Neo4j
- Hands-On: Basic Queries in Neo4j With Cypher - Part 1
- Hands-On: Basic Queries in Neo4j With Cypher - Part 2
- Hands-On: Path Analytics in Neo4j Using Cypher - Part 1
- Hands-On: Path Analytics in Neo4j Using Cypher - Part 2
- Hands-On: Connectivity Analytics in Neo4j With Cypher
- About the Supplementary Resources
- Downloading, Installing, and Running Neo4j - Supplementary Resources
- Getting Started With Neo4j - Supplementary Resources
- Adding to and Modifying a Graph - Supplementary Resources
- Download datasets used in this Graph Analytics with Neo4j
- Importing Data Into Neo4j - Supplementary Resources
- FAQ
- Basic Queries in Neo4j With Cypher - Supplementary Resources
- Path Analytics in Neo4j With Cypher - Supplementary Resources
- Connectivity Analytics in Neo4j with Cypher - Supplementary Resources
- Assignment: Practicing Graph Analytics in Neo4j With Cypher
- Download All Neo4j Supplementary Resources (PDFs)
- Quiz: Graph Analytics With Neo4j
- Assessment Questions on 'Practicing Graph Analytics in Neo4j With Cypher'
- Computing Platforms for Graph Analytics
- Introduction: Large Scale Graph Processing
- A Parallel Programming Model for Graphs
- Pregel: The System That Changed Graph Processing
- Giraph and GraphX
- Beyond Single Vertex Computation
- Introduction to GraphX: Hands-On Demonstrations
- Hands On: Building a Graph
- Hands On: Building a Degree Histogram
- Hands On: Plot the Degree Histogram
- Hands On: Network Connectedness and Clustering Components
- Hands On: Joining Graph Datasets
- Datasets and Libraries for Example of Analytics Hands On
- Download all of the readings for this section as a PDF
- Hands On: Building a Graph Reading
- Hands On: Building a Degree Histogram Reading
- Hands On: Plot the Degree Histogram Reading
- Hands On: Network Connectedness and Clustering Components Reading
- Hands On: Joining Graph Datasets Reading
- Using GraphX
Summary of User Reviews
Discover the power of big data with Coursera's Big Data Graph Analytics course. This course has received high praise for its comprehensive approach to understanding the complexities of big data. Users have been impressed with the course's ability to teach complex concepts in a clear and concise manner.Key Aspect Users Liked About This Course
The course's use of real-world examples and case studies to reinforce the concepts taught.Pros from User Reviews
- Comprehensive approach to understanding big data
- Clear and concise explanations of complex concepts
- In-depth coverage of graph analytics
- Use of real-world examples and case studies to reinforce concepts
- Engaging and knowledgeable instructors
Cons from User Reviews
- Some users found the course content to be too technical
- A few users felt that the course could have provided more hands-on practice
- Limited interaction with instructors and other students
- High workload and time commitment
- Some users found the course to be too basic for their level of experience
Amarnath Gupta
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