Graph Analytics for Big Data Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a comprehensive introduction to graph analytics for big data, combining foundational theory with hands-on practice using industry-standard tools. Learners will explore graph theory fundamentals, apply key analytics techniques, and work with platforms like Neo4j and GraphX to solve real-world problems. The course is designed for self-paced learning and requires approximately 10 hours of total time commitment.
Module 1: Welcome to Graph Analytics
Estimated time: 0.2 hours
- Introduction to the course
- Course objectives and structure
- Overview of graph analytics applications
Module 2: Introduction to Graphs
Estimated time: 2 hours
- Basics of graph theory
- Real-world applications of graphs
- Impact of big data characteristics on graph structures
Module 3: Graph Analytics
Estimated time: 3 hours
- Path analytics and shortest path algorithms
- Connectivity analysis in networks
- Community detection techniques
- Centrality measures and their significance
Module 4: Graph Analytics Techniques
Estimated time: 2 hours
- Introduction to Neo4j
- Using Cypher query language
- Practical graph querying and analysis
- Performing analyses on graph networks
Module 5: Computing Platforms for Graph Analytics
Estimated time: 2 hours
- Introduction to Pregel
- Using Apache Giraph for large-scale processing
- Implementing graph algorithms with GraphX
Module 6: Final Project
Estimated time: 1 hour
- Model a real-world network using graph structures
- Apply graph analytics techniques to extract insights
- Submit analysis report for certificate eligibility
Prerequisites
- Basic understanding of data structures and algorithms
- Familiarity with programming concepts
- Some exposure to big data concepts recommended
What You'll Be Able to Do After
- Understand the fundamentals of graph theory and its applications in big data
- Model real-world problems using graph structures
- Apply graph analytics techniques such as path finding, connectivity, and community detection
- Utilize Neo4j and Cypher for practical graph querying
- Implement large-scale graph processing using GraphX and similar frameworks