CSC3833 : Data Visualization and Visual Analytics
- Offered for Year: 2024/25
- Module Leader(s): Dr Sara Johansson Fernstad
- Lecturer: Dr Xinhuan Shu
- Owning School: Computing
- Teaching Location: Newcastle City Campus
Semesters
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 | |
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 time series data.
• Interactive techniques for visualization.
• Visualization design
• What makes a good visualization.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 11 | 1:00 | 11:00 | PIP lectures (underpinned by online material). |
| Scheduled Learning And Teaching Activities | Practical | 11 | 1:00 | 11:00 | Exercises set with drop-in sessions for PIP support in computer classrooms. |
| Guided Independent Study | Project work | 5 | 1:00 | 5:00 | Project work - Reflective report preparation |
| Guided Independent Study | Project work | 22 | 1:00 | 22:00 | Coursework preparation |
| Guided Independent Study | Independent study | 11 | 1:00 | 11:00 | Lecture follow-up |
| Guided Independent Study | Independent study | 40 | 1:00 | 40:00 | 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 | 100 | Reflective report on case study tasks/problems 2000 words |
Formative Assessments
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. |
Assessment Rationale And Relationship
The formative assessment builds on the lecture work and provide opportunities for students to test and develop their understanding of the taught material. The summative assessment is then based on a case study, using real world data, allowing students to explore practical application of the techniques and algorithms that have been learned through taught material and formative assessment. The reflective report offers students the opportunity to evaluate and reflect upon their use of taught theory in the problem-based study.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC3833's Timetable