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Curricular information is subject to change
1. Demonstrate an understanding of human visual perception & how it can be exploited to design effective visualisations
2. Identify visualisation approaches suitable for specific data types (including tabular data, spatial data, and network data).
3. Critically evaluate different visualisation approaches as applied to particular tasks
4. Implement interactive visualisation approaches using a programming language
Student Effort Type | Hours |
---|---|
Autonomous Student Learning | 75 |
Lectures | 22 |
Computer Aided Lab | 14 |
Total | 111 |
Students should have previously successfully completed the module “COMP30760: Data Science in Python - DS”.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Not yet recorded. |
Resit In | Terminal Exam |
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Autumn | No |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.