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Graph Data Modeling Course Description for the

This website contains material for a 14-week undergraduate course on graph data modeling.

Our intent is to not require any prerequisites for this course so a wide range of students can use this course for self-paced learning. However, instructors make require students to have a basic understanding of datatypes typically taught in an undergraduate data structures course. These datatypes include the use of strings, integers, floats, sets, lists, and arrays as datatypes use in attributes.

Course Title: Graph Data Modeling

Summary

This course explores the foundational principles and advanced techniques of graph data modeling, to progressively develop students' skills in data modeling. Students will begin by understanding the fundamental concepts and terminology, then advance to applying graph modeling strategies across diverse domains, analyzing complex relationships, evaluating modeling trade-offs, and ultimately creating robust, domain-specific graph data models.

Learning Objectives

Upon successful completion of the course, students will be able to:

  1. Remember key graph modeling concepts, including nodes, edges, properties, and paths.
  2. Understand the significance of graph data models in representing real-world systems and relationships.
  3. Apply graph modeling techniques to domains such as customer management, healthcare, supply chains, and fraud detection.
  4. Analyze domain-specific requirements to identify optimal graph structures and relationships.
  5. Evaluate trade-offs in architectural and performance considerations for graph models.
  6. Create advanced and scalable graph models for dynamic and evolving systems, including digital twins, causality, and metadata.

Topics Covered

  • Foundations of Graph Data Modeling: Introduction to graph concepts, ISO GQL, and future trends.
  • Core Concepts: Nodes, edges, properties, and paths.
  • Domain-Specific Modeling: Customer data, products, healthcare, security threats, and fraud.
  • Advanced Topics: Bitemporal modeling, causality, supply chains, and metadata.
  • Future Directions: Model evolution, scalability, sustainability, and AI integration.

Course Activities and Assessments

  • Case studies on real-world graph applications.
  • Hands-on exercises with graph databases and modeling tools.
  • Group projects to design domain-specific graph models.
  • Quizzes and exams assessing conceptual understanding and practical skills.
  • A capstone project focused on creating a comprehensive graph model for a chosen domain.

This course is designed for students in computer science, data science, or related fields who are interested in mastering graph databases and their applications in modern data-driven systems.

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