COMP30850 Network Analysis

Academic Year 2024/2025

The objective of this module is to provide undergraduate students with a thorough introduction to network analysis from a Computer Science perspective. The module will cover the basic concepts and key algorithms in network analysis, and discuss their use in the context of many real-world applications across different domains. Throughout the course, students will gain familiarity with various network analysis techniques and learn how to apply them effectively on real datasets using the Python programming language. The evaluation for this module involves an individual project assignment and a final practical class test.

NOTE: Students taking this module must have previously taken the module COMP30760 "Data Science in Python".

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Curricular information is subject to change

Learning Outcomes:

On completion of this module, students will be able to:
1. Understand the core concepts and algorithms in network analysis.
2. Create appropriate network representations from real-world data.
3. Interpret, compare, and critically appraise different network representations.
4. Competently apply packages and tools for network analysis and visualisation.

Indicative Module Content:

The topics covered by this module may include:
- Basic concepts in graphs and networks
- Applications of network analysis
- Representing data as networks
- Network measures and metrics, including centrality
- Path problems and algorithms
- Network visualisation
- Dynamic networks
- Social media networks

Student Effort Hours: 
Student Effort Type Hours
Lectures

12
Practical

10
Autonomous Student Learning

80
Total

102
Approaches to Teaching and Learning:
Practical Labs; Continuous assessment 
Requirements, Exclusions and Recommendations
Learning Requirements:

Students must have previously successfully completed the module “COMP30760: Data Science in Python - DS”.


Module Requisites and Incompatibles
Co-requisite:
COMP30760 - Data Science in Python - DS


 
Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Practical individual assignment Week 7 Alternative linear conversion grade scale 40% No

50

 No
Exam (Open Book): Two hour practical exam. Scheduled during Fieldwork/Study period. Week 9 Alternative linear conversion grade scale 40% No

50

 No

Carry forward of passed components
No
 
Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Laura Dunne Tutor