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Curricular information is subject to change
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 practical methods and tools for network analysis and visualisation.
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 Type | Hours |
---|---|
Lectures | 12 |
Practical | 12 |
Autonomous Student Learning | 70 |
Total | 94 |
Students must have previously successfully completed the module “COMP30760: Data Science in Python - DS”.
Resit In | Terminal Exam |
---|---|
Summer | No |
• Feedback individually to students, post-assessment
Not yet recorded.
Name | Role |
---|---|
Suchana Datta | Tutor |
Laura Dunne | Tutor |
Negin Zarbakhsh | Tutor |
Exam Sem. 2 (ALU) | Offering 1 | Week(s) - 25 | Thurs 09:00 - 09:50 |
Exam Spring (ALU) | Offering 1 | Week(s) - 28 | Thurs 09:00 - 11:50 |
Laboratory | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25 | Thurs 10:00 - 10:50 |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25 | Tues 11:00 - 11:50 |
Laboratory | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25 | Tues 13:00 - 13:50 |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25 | Wed 14:00 - 14:50 |