CSC3833 : Data Visualization and Visual Analytics
- Offered for Year: 2023/24
- Module Leader(s): Dr Sara Fernstad
- Lecturer: Dr Alma Cantu
- Owning School: Computing
- Teaching Location: Newcastle City Campus
Semesters
| Semester 1 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
Aims
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.
Outline Of Syllabus
• 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 hierarchical data.
• Interactive techniques for visualization.
• What makes a good visualization.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 11 | 1:00 | 11:00 | Lecture follow-up |
| Scheduled Learning And Teaching Activities | Lecture | 11 | 1:00 | 11:00 | PIP lectures (underpinned by online material). |
| 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 | Project work | 5 | 1:00 | 5:00 | Reflective report preparation |
| Guided Independent Study | Project work | 22 | 1:00 | 22:00 | Coursework portfolio preparation |
| Guided Independent Study | Independent study | 29 | 1:00 | 29:00 | Background reading |
| Total | 100:00 |
Teaching Rationale And Relationship
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.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Case study | 1 | M | 85 | Report on case study tasks/problems 1000-2000 words |
| Report | 1 | M | 15 | A reflective report and evaluation on the skills gained summarising the portfolio of evidence produced by the problem-based activities. (up to 1000 words) |
Assessment Rationale And Relationship
The assessment is based on case studies, 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 draw together the overall learning experience from the entire data analytical and visualization pipeline
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC3833's Timetable