Run by School of Computer Science and Electronic Engineering

20.000 Credits or 10.000 ECTS Credits

Semester 1 & 2

Organiser: Prof Jonathan Roberts

Information Visualization is telling stories from data. The aim of this module is to introduce principles of information visualisation, develop critical evaluation skills, learn about visual design methods (such as the Five Design-Sheet sketching method), and use programming skills to create and present a new visualisation. Students discover stories hidden in some chosen data, and design their own visualisation to graphically depict the information, which is often presented alongside text, tables and other suitable methods. This is done to tell the story of that data set. Students learn critical evaluation skills, learn about different Information Visualization designs, about data types, learn how data can be mapped into visual artefacts and its value encoded through (e.g.,) size, shape, length, position, colour. They gain foundational knowledge and skills to design new innovative data Information Visualisations through planning by sketching and implementing their designs.

Indicative content includes:

• The history and future of Information Visualization; the challenges of Information Visualization; tasks; user, perception, data types.
• Looking at data. Data capture and problems of capturing data. Selection/abstraction of data (aggregation, sampling; binning; cropping); Big data challenges.
• Understand current visualisation techniques, including traditional plots (bar, line, scatter etc.), parallel coordinate plots, treemaps, re-orderable matrix; scatter plot matrix.
• Perception, visual mapping and interpretation; understanding how humans perceive information. Encoding of value; Encoding of relation; Models: Bertin, Mackinlay (Quantitative, Ordinal, and Categorical), Semiotics.
• Design of visualisations, Five Design-Sheet method; Consideration and critical thinking around alternative solutions.
• Storytelling and Presentation. Display, layout and presentation of visual information. Interaction and static presentation. Techniques such as focus-and-context, size adjustment, scale and distortion; multiple views and composite displays.

threshold

Equivalent to 50%. Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.

good

Equivalent to the range 60%-69%. Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.

excellent

Equivalent to the range 70%+. Assemble critically evaluated, relevent areas of knowledge and theory to constuct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.

1. Critically evaluate Information Visualization techniques and suitability for a given purpose.

2. Apply information visualization techniques (Representation, Presentation, and Interaction) to develop new visualization techniques.

3. Evaluate and develop alternative visualization design solutions through sketching and planning.

4. Select appropriate visual techniques for a particular context.

5. Describe different aspects of data, identify, analyse and evaluate component parts.

Type	Name	Description	Weight
	Analysis of data		25.00
	Technical design plan		25.00
	Created visualisation		25.00
	Storytelling and reflection		25.00

		Hours
Lecture	Traditional lectures (1 hr x 24 weeks)	24
Practical classes and workshops	Practical exercises and group discussions.	24
Private study	Tutor-directed private study including individual assessments.	152

• Literacy - Proficiency in reading and writing through a variety of media
• Numeracy - Proficiency in using numbers at appropriate levels of accuracy
• Computer Literacy - Proficiency in using a varied range of computer software
• Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
• Exploring - Able to investigate, research and consider alternatives
• Information retrieval - Able to access different and multiple sources of information
• Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
• Presentation - Able to clearly present information and explanations to an audience. Through the written or oral mode of communication accurately and concisely.
• Argument - Able to put forward, debate and justify an opinion or a course of action, with an individual or in a wider group setting
• Self-awareness & Reflectivity - Having an awareness of your own strengths, weaknesses, aims and objectives. Able to regularly review, evaluate and reflect upon the performance of yourself and others

• Apply knowledge and understanding of the specialist cognate area of computer systems engineering in an international context;
• Formulate and analyse requirements and practical constraints of products, processes and services, place them in an engineering context and manage their implementation;
• Solve problems logically and systematically;
• Assess and choose optimal methods and approaches for the specification, design, implementation and evaluation of engineering solutions.
• Plan, budget, organise and manage people and resources;
• Agree objectives and work plans with individuals;
• Analyse and display data using appropriate methods and mathematical techniques;
• Deploy theory in design, implementation and evaluation of systems
• Specify, design or construct computer-based systems
• Deploy tools effectively
• Development of general transferable skills
• System Design
• Knowledge and/or understanding of appropriate scientific and engineering principles

Talis Reading list

http://readinglists.bangor.ac.uk/modules/ice-4121.html

Reading list

• Five Design-Sheets: Creative Design and Sketching for Computing and Visualisation. Authors: Roberts, Jonathan C, Headleand, Christopher J., Ritsos, Panagiotis D. Springer. 2017

Compulsory in courses:

• G5BB: MSc Advanced Data Science year 1 (MSC/ADS)

Optional in courses:

• H117: MComp Computer Science year 4 (MCOMP/CS)
• G4AS: MSc Advanced Computer Science year 1 (MSC/ACS)
• G5BC: MSc Computing for Data Science year 1 (MSC/CDS)