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