Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Students will learn and acquire skills in data exploration and visualization. By the end of the module, they will be able to take raw data sets, clean them, structure them and choose suitable methods for visualizing them. They will also acquire theoretical knowledge of the underpinning descriptive statistics and the basics of human perception for cognition.
• Topics from:
• Descriptive statistics for data sets.
• The visualization pipeline.
• Human perception and cognition.
• Visualization of numerical data and categorical data.
• Visualization of geographical data.
• Visualization of time series data.
• Interactive techniques for visualization.
• Visualization design
• What makes a good visualization.
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 11 | 1:00 | 11:00 | PIP lectures (underpinned by online material). |
| Guided Independent Study | Assessment preparation and completion | 5 | 1:00 | 5:00 | Project work - Reflective report preparation |
| Guided Independent Study | Assessment preparation and completion | 22 | 1:00 | 22:00 | Coursework |
| Scheduled Learning And Teaching Activities | Practical | 11 | 2:00 | 22:00 | Exercises set with drop-in sessions for PIP support in computer classrooms. |
| Guided Independent Study | Independent study | 11 | 1:00 | 11:00 | Lecture follow-up |
| Guided Independent Study | Independent study | 29 | 1:00 | 29:00 | Background reading |
| Total | 100:00 |
The teaching methods combine present in person lectures, underpinned by online material, with practical sessions so that students can explore the topics covered in both a theoretical and practical context. Lectures outline the underlying principles, algorithms and theory, while practical lab work encourages students to implement the algorithms using real-world data, in terms of both visualizing data and using visualizations as part of the analytical process.
The format of resits will be determined by the Board of Examiners
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Case study | 1 | M | 100 | Reflective report on case study tasks/problems 2000 words |
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Prob solv exercises | 1 | M | The formative assessments include a combination of online quizzes, testing the taught material and visualization design exercises. |
The formative assessment builds on the lecture work and provide opportunities for students to test and develop their understanding of the taught material. The assessment is based on a case study, using real world data, allowing students to explore practical application of the techniques and algorithms that have been learned. The reflective report offers students the opportunity to evaluate and reflect upon their use of taught theory in the problem-based study.