What will you in the Graph Analytics for Big Data Course
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 analysis, community detection, and centrality measures.
Utilize tools like Neo4j and its Cypher query language for practical graph querying and analysis.
Implement large-scale graph processing using frameworks like GraphX and Giraph.
Program Overview
1. Welcome to Graph Analytics
Duration: 13 minutes
Introduction to the course and its objectives.
2. Introduction to Graphs
Duration: 2 hours
Basics of graph theory and its real-world applications.
Understanding the impact of big data characteristics on graphs
3. Graph Analytics
Duration: 3 hours
In-depth exploration of graph analytics techniques.
Topics include path analytics, connectivity, community detection, and centrality measures.
4. Graph Analytics Techniques
Duration: 2 hours
Hands-on experience with Neo4j and Cypher for graph analysis.
Performing various analyses on graph networks
5. Computing Platforms for Graph Analytics
Duration: 2 hours
Introduction to large-scale graph processing frameworks like Pregel, Giraph, and GraphX.
Implementing graph algorithms at scale.
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Job Outlook
Data Scientists & Analysts: Enhance your ability to analyze complex network data.
Software Engineers: Gain skills in graph databases and large-scale data processing.
Business Intelligence Professionals: Leverage graph analytics for deeper insights into interconnected data.
Researchers & Academics: Apply graph theory concepts to various fields such as biology, social sciences, and urban planning.
Specification: Graph Analytics for Big Data
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