Rhedir gan School of Computer Science and Electronic Engineering

20.000 Credyd neu 10.000 Credyd ECTS

Semester 1 a 2

To enable students to analyse data and break it down into its key components. To enable students to learn about current visualisation designs and gain skills of designing interfaces that visualise data and allow users to interact and explore that information, and develop skills of critiquing and evaluating artefacts.

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 and interpretation; understanding how humans perceive information. Encoding of value; Encoding of relation; Models: Bertin, Mackinlay (Quantitative, Ordinal, and Categorical), Semiotics.
• Design of visualisations, dashboards, and considering alternative solutions and critical analysis of these visualisations.
• Interaction and exploration, looking at focus + context and distortion technique; multiple views and composite interaction; brushing; animation.

trothwy

Equivalent to 40%. 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.

da

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.

ardderchog

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. Design, implement and evaluate interactive visualisation tools for a given data set using appropriate design guidelines.

2. Illustrate Information Visualization techniques and select a suitable type for a given purpose.

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

Math	Enw	Disgrifiad	Pwysau
	Technical design plan		50.00
	Visualisation implementation		50.00

		Oriau
Lecture	Lectures.	24
Private study	Private study.	152
Laboratory	Lab based exercises and individual working.	24

• Llythrennedd - Medrusrwydd mewn darllen ac ysgrifennu drwy amrywiaeth o gyfryngau
• Hunanreolaeth - Gallu gweithio mewn ffordd effeithlon, prydlon a threfnus. Gallu edrych ar ganlyniadau tasgau a digwyddiadau, a barnu lefelau o ansawdd a phwysigrwydd
• Archwilio - Gallu ymchwilio ac ystyried dewisiadau eraill
• Dadansoddi Beirniadol & Datrys Problem - Gallu dadelfennu a dadansoddi problemau neu sefyllfaoedd cymhleth. Gallu canfod atebion i broblemau drwy ddadansoddiadau ac archwilio posibiliadau
• Cyflwyniad - Gallu cyflwyno gwybodaeth ac esboniadau yn glir i gynulleidfa. Trwy gyfryngau ysgrifenedig neu ar lafar yn glir a hyderus.
• Gwaith Tîm - Gallu cydweithio'n adeiladol ag eraill ar dasg gyffredin, ac/neu fod yn rhan o dîm gweithio o ddydd i ddydd
• Dadl - Gallu cyflwyno, trafod a chyfiawnhau barn neu lwybr gweithredu, naill ai gydag unigolyn neu mewn grwˆp ehangach

• Identify emerging technologies and technology trends;
• 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;
• Access and synthesize information and literature sources;
• Analyse and display data using appropriate methods and mathematical techniques;
• Demonstrate familiarity with relevant subject specific and general computer software packages.
• Knowledge and understanding of facts, concepts, principles & theories
• Problem solving strategies
• Analyse if/how a system meets current and future requirements
• Development of general transferable skills
• Defining problems, managing design process and evaluating outcomes
• System Design
• Knowledge and understanding of mathematical principles

Gorfodol mewn cyrsiau:

• H116: BSc Applied Data Science (Degree Apprenticeship) year 3 (BSC/ADS)
• H114: BSc Data Science and Visualisation year 3 (BSC/DSV)