Skip to main content

Data Visualisation:  2022-2023

Lecturer

Degrees

Schedule A2(CS&P)Computer Science and Philosophy

Schedule B1 (CS&P)Computer Science and Philosophy

Schedule A2Computer Science

Schedule B1Computer Science

Schedule A2(M&CS)Mathematics and Computer Science

Schedule B1(M&CS)Mathematics and Computer Science

Hilary TermMSc in Advanced Computer Science

Term

Overview

Well-designed visualisations capitalise on human facilities for processing visual information and thereby improve comprehension, memory, inference, and decision making. In addition, the advent of the so-call “explosion of Big Data” has increased the needs of effective visualisation systems for data analysis and communication. In this course we will study techniques and algorithms for creating effective visualisations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. The course is targeted both towards students interested in using visualisation in their own work, as well as students interested in building better visualisation tools and systems.

Learning outcomes

The lectures outlined below have been designed so that, by the end of course, students will have:

• an understanding of key visualization techniques and theory, including data models, graphical perception and methods for visual encoding and interaction.

• exposure to a number of common data domains and corresponding analysis tasks, including exploratory data analysis and network analysis.

• practical experience building and evaluating visualisation systems.

Feedback

Students are formally asked for feedback at the end of the course. Students can also submit feedback at any point here. Feedback received here will go to the Head of Academic Administration, and will be dealt with confidentially when being passed on further. All feedback is welcome.