Run by School of Computer Science and Electronic Engineering
20.000 Credits or 10.000 ECTS Credits
Semester 1 & 2
Overall aims and purpose
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.
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.
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.
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.
Design, implement and evaluate interactive visualisation tools for a given data set using appropriate design guidelines.
Illustrate Information Visualization techniques and select a suitable type for a given purpose.
Describe different aspects of data, identify, analyse and evaluate component parts.
|Technical design plan||50.00|
Teaching and Learning Strategy
Lab based exercises and individual working.
- Literacy - Proficiency in reading and writing through a variety of media
- 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
- 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.
- Teamwork - Able to constructively cooperate with others on a common task, and/or be part of a day-to-day working team
- Argument - Able to put forward, debate and justify an opinion or a course of action, with an individual or in a wider group setting
Subject specific skills
- 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
Courses including this module
Compulsory in courses:
- H116: BSc Applied Data Science (Degree Apprenticeship) year 3 (BSC/ADS)
- H114: BSc Data Science and Visualisation year 3 (BSC/DSV)