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Module

CSC3833 : Data Visualization and Visual Analytics

  • Offered for Year: 2021/22
  • Module Leader(s): Professor Nick Holliman
  • 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
Scheduled Learning And Teaching ActivitiesLecture111:0011:00Mixed mode synchronous lectures, underpinned by online material.
Guided Independent StudyAssessment preparation and completion111:0011:00Lecture follow-up
Scheduled Learning And Teaching ActivitiesPractical112:0022:00Exercises set with optional drop-in sessions for support in computer classrooms.
Guided Independent StudyProject work221:0022:00Coursework portfolio preparation
Guided Independent StudyProject work51:005:00Reflective report preparation
Guided Independent StudyIndependent study291:0029:00Background reading
Total100:00
Teaching Rationale And Relationship

The teaching methods combine mixed mode lectures 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 study1M90For each topic/problem students will provide a report that contributes to their portfolio (1000 words) inclu x1 visualization per ex
Report1M10A reflective report on the skills gained summarising the portfolio of evidence produced by the problem-based activities. (200 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