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COMP30850

Academic Year 2024/2025

Network Analysis (COMP30850)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Assoc Professor Derek Greene
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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".

About this Module

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
Autonomous Student Learning

80

Lectures

12

Practical

10

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

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Exam Mid-term (ALU) Offering 1 Week(s) - 28 Thurs 09:00 - 11:50
Spring Laboratory Offering 1 Week(s) - 20, 21, 22, 23, 24, 25 Thurs 10:00 - 10:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25 Tues 11:00 - 11:50
Spring Laboratory Offering 1 Week(s) - 20, 21, 22, 23, 24, 25 Tues 13:00 - 13:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25 Wed 14:00 - 14:50