Postgraduate Course: Data Visualisation (INFR11190)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|Summary||Only available to Informatics MSc students on the Data Science or Design Informatics MSc programmes.
This course teaches how to visually explore data and how to criticise, design and implement data visualisations. It teaches the fundamentals of human perception and data visualisation, exploratory data analysis and the importance of interaction in exploration, techniques for data visualisation of specific data sets (networks, temporal data, geographic data, etc..), and storytelling. Group work (50%) includes exploring data using existing visualisation tools, designing and creating visualisation prototypes for exploration or presentation. Individual work (50%) includes criticising data visualisations.
Only available to Informatics MSc students on the Data Science or Design Informatics MSc programmes.
This course teaches general knowledge about theory, application, design, and evaluation of visualisations. The goal of the course is to enable students to understand the potential of visualisations and how interactive visualisation interfaces can support the workflow of data analysis.
During most of the course, students will work in groups of two to three students using real-world datasets they find themselves or which are provided. Groups will go through all the stages of the exploration and visualisation design process, in alignment with the above listed learning outcomes; explore data and make initial findings, critique the tools and list shortcomings and possible future features, create custom visualisation designs for exploration or presentation, present the visualisations. For a final presentation, each group is expected to present a comprehensive visualisation design project, insights gained, and critical reflections.
The course aims for 11 lectures, each targeting a set of principles in data visualisations, and organized as shown below. Lectures will be held as a flipped classroom where lectures require listening to a previously recorded lecture, or reading a book chapter or representative (easy to understand) scientific paper.
1. Foundations of data visualisation: Perception, visual variables, exploratory data analysis, explanatory visualisation, scenarios, tools.
2. Visualisation Techniques: visualisations for statistical data, hierarchies and networks, temporal data, geographical data, multivariate data, etc.
3. Advanced topics: data-driven storytelling, interaction techniques, and evaluation techniques.
4. Guest lectures (to be decided upon availability): data physicalisation, visual analytics, geo-visualisation, visualisation in immersive environments, HCI for visualisation, etc.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| Only available to Informatics MSc students on the Data Science or Design Informatics MSc programmes.
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- Analysis: Identify and describe a visualisation challenge in terms of context, stakeholders, data, and tasks.
- Design: Design and implement a visualisation through one of various media (website, physicalization, infographic, etc.) and through a self-chosen set of tools. Visualization designs are meant to match an earlier identified challenge.
- Evaluation: Critically reflect on a visualisation design and suggest constructive solutions.
|- Segel, Heer, Narrative Visualization, 2010, https://egerber.mech.northwestern.edu/wp-content/uploads/2015/02/Narrative_Visualization.pdf|
- Bertin, Semiology of Graphics, 1987
- Nussbaumer: Storytelling with Data, 2017: http://www.storytellingwithdata.com
- Tufte: Visual Evidence: Images and Quantities, Evidence and Narrative, 1997
- Colin Ware: Information Visualization, 1999
- Tamara Munzner: Visualization Analysis & Design, 2014
- Scott Murray: Interactive Data Visualization for the Web An Introduction to designing with D3, 2013
- Manual Lima: Visual Complexity Mapping Patterns of Information, 2011
- Ben Shneiderman: The eyes have it: A task by data type taxonomy for information visualizations, 1996
- Panday et al: How deceptive are deceptive visualizations: An empirical analysis of common distortion techniques, 2015
- Von Landesberger et al: Visual analysis of large graphs: state of the art and future research challenges, 2011
- Tamara Munzner: A nested model for visualization design and validation, 2009
|Graduate Attributes and Skills
|| - Problem analysis: analyse the problem related to exploring and communicating data in a specific context
- Critical thinking: thinking critically about the effectiveness of data visualisation for a given challenge, in a given context.
- Creativity: searching for (novel) alternative visualisation solutions to a specific challenge
- Visual design: sensitivity about how to use visual design skills to improve visual communication
- Teamwork: discussing problems and ideas within a group of students.
- Verbal communication: presenting and discussing a data visualisation, avoiding common pitfalls in communicating with data visualisations.
||Only available to Informatics MSc students on the Data Science or Design Informatics MSc programmes.
|Keywords||data visualisation,exploratory data analysis,visual design,data science,interface design
|Course organiser||Dr Benjamin Bach
Tel: (0131 6)51 3076
|Course secretary||Ms Lindsay Seal
Tel: (0131 6)50 2701