Postgraduate Course: Data Visualisation (INFR11190)
Course Outline
| 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 |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| 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 criticize, design and implement data visualizations. It teaches the fundamentals of human perception and data visualization, exploratory data analysis and the importance of interaction in exploration, techniques for data visualization of specific data sets (networks, temporal data, geographic data, etc..), and storytelling. Group work (50%) includes exploring data using existing visualization tools, designing and creating visualization prototypes for exploration or presentation. Individual work (50%) includes criticizing data visualizations.
Programming knowledge and experience with JavaScript will very helpful but are not required. The course is open only to students from the following programs: Data Science and Design Informatics.
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| Course description |
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 visualizations. The goal of the course is to enable students to understand the potential of visualizations and how interactive visualization interfaces can support the workflow of data analysis.
The course will enable students to describe a visualization problem, to explore the data using visualizations, to discuss and design appropriate visualization concepts, and to implement and critically reflect on them. The course is designed for an interdisciplinary audience, targeting students with a background in design, informatics, and other areas. General programming skills are not required but some relevant JavaScript library (D3.js, https://d3js.org) are provided during the first weeks of the course. Besides interactive visualizations, students can opt to create static visualizations (infographics, data comics, posters, etc), data physicalizations, or any any other form discussed in the course.
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 visualization 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 visualization designs for exploration or presentation, present the visualizations. For a final presentation, each group is expected to present a comprehensive visualization design project, insights gained, and critical reflections.
The course aims for 11 lectures, each targeting a set of principles in data visualizations, 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.
Lecture topics:
1. Foundations of data visualization: Perception, visual variables, exploratory data analysis, explanatory visualization, scenarios, tools.
2. Visualization Techniques: visualizations 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 physicalization, visual analytics, geo-visualization, visualization in immersive environments, HCI for visualization, etc.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Only available to Informatics MSc students on the Data Science or Design Informatics MSc programmes. |
Course Delivery Information
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| Academic year 2019/20, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 10,
Revision Session Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
64 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam __---___%
Practical Exam ____% (for courses with programming exams)
Coursework __100___%
1) A1: Group Assignment (50%): Students work in groups of 2-3 students to analyse, design, and implement a visualization prototype. This can include interactive web visualizations, data physicalizations, data comics, infographics, etc.
2) A2: Individual Assignment (50%): Each student will analyse problems with a given set of visualizations and propose improvements.
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| Feedback |
Feedback will be given to students
- by tutors in each of the three tutorials
- Individual written feedback per assignment handed in.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Analysis: Identify and describe a visualization challenge in terms of context, stakeholders, data, and tasks.
- Design: Design and implement a visualization 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 visualization design and suggest constructive solutions.
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Reading List
- 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
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Additional Information
| Graduate Attributes and Skills |
- Problem analysis: analyze the problem related to exploring and communicating data in a specific context
- Critical thinking: thinking critically about the effectiveness of data visualization for a given challenge, in a given context.
- Creativity: searching for (novel) alternative visualization 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 visualization, avoiding common pitfalls in communicating with data visualizations.
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| Special Arrangements |
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 |
Contacts
| Course organiser | Dr Benjamin Bach
Tel: (0131 6)51 3076
Email: |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 2701
Email: |
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