Postgraduate Course: Data Visualisation for Professionals (INFD11018)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Course type||Online Distance Learning
||Availability||Not available to visiting students
|Summary||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.
Programming knowledge and experience are not required but can be acquired and deepened throughout the course.
This course teaches general knowledge about theory, application, design, and evaluation of visualisations. The goal of the course is to enable participants to understand the potential of visualisations and how visualisation and interactive interfaces can support the workflow of data analysis and communication.
More information can be found on our course website: https://datavis-online.github.io
The course will enable participants to describe a visualisation problem, to explore the data using visualisations, to discuss and design appropriate visualisation concepts, and to implement and critically reflect on them. The course is designed for an interdisciplinary audience, targeting participants with a background in design, data analysis, and other areas.
Participants will have to bring their own devices. No specific hardware is require. Most tools will run in a web-browser.
Individuals will work on their own projects, alone or in groups, and 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.
The course aims for 10 lectures, each targeting a set of principles in data visualisations, and organised 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, deception, visualisation 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, evaluation techniques, geo-visualisation.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- Analyze: Describe a challenge for a visualisation project and the reasons why visualisation is required. Break down the project considering its context (target audience, usage scenario), potential tasks that the visualisation should facilitate, and the characteristics of the data set.
- Design : Create a visualisation through one of various media (website, interactive, infographic, etc.) and through a self-chosen set of tools. Visualisation designs are meant to match an earlier identified challenge.
- Evaluation: Critically reflect on a visualisation design and suggest constructive solutions.
- Apply: Competently apply a wide range of visualisation techniques and tools, also knowing their particular features and drawbacks.
|- Segel, Heer, Narrative Visualization, 2010,|
- 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
||Only open to Distance Learning students
|Keywords||data visualisation,exploratory data analysis,visual design,data science
|Course organiser||Dr Benjamin Bach
Tel: (0131 6)51 3076
|Course secretary||Ms Lindsay Seal
Tel: (0131 6)50 2701