Run by School of Computer Science and Electronic Engineering

20.000 Credits or 10.000 ECTS Credits

Semester 1 & 2

Displaying analytical data can be important to communicating the correct information. Taking data and then visualising this to show deeper analysis requires an understanding of the underlying algorithms and being able to code this visually.

This module will provide learners with the ability to visualise graphical data by using advanced algorithms. The students will build on an earlier Digital Graphics module and learn how to delve deeper into their own analysis and how to represent this in a clear visual manner.

Indicative content includes:

• Understand the nature of analytical data
• Understand the visualisation process of data
• Use and understand a range of Java Script elements
• Understand how to use the HTML Canvas to display algorithmic data
• How to draw more precise elements using html Canvas and Java Script
• Manipulation of images (Masks, Brightness, Colour, Effects) to highlight data
• Simple animation based on data (predict forecast, show growth/loss etc.)
• Interpreting a business scenario to create a professional visualisation.
• Review and justify visualisation and development decisions made.

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.

threshold

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.

1. Produce visual representations for a set of supplied data using Canvas and Java Script in a suitable visual form.

2. Justify the visual representation choices made.

3. Analyse an informative scenario and create a set (or sets) of meaningful data.

4. Display a meaningful representation of analysed data using visual tools such as Canvas and Java Script.

5. Review the completed solution.

Type	Name	Description	Weight
	JavaScript Canvas		40.00
	HTML Report		60.00

		Hours
Private study	The tutor directed student learning will be supported by online learning materials hosted or signposted on the VLE.	140
Lecture	The classroom-based element will include student-centred learning methods such as interactive lectures, case studies, group discussions and practical workshops.	60

• 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
• 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.

• Knowledge and understanding of facts, concepts, principles & theories
• Use of such knowledge in modelling and design
• Problem solving strategies
• Analyse if/how a system meets current and future requirements
• Deploy theory in design, implementation and evaluation of systems
• Specify, design or construct computer-based systems
• Evaluate systems in terms of quality and trade-offs
• Deploy tools effectively
• Development of general transferable skills
• Deploy systems to meet business goals
• Methods, techniques and tools for information modelling, management and security
• Specify, deploy, verify and maintain information systems
• System Design
• Knowledge and/or understanding of appropriate scientific and engineering principles
• Knowledge and understanding of mathematical principles
• Knowledge and understanding of computational modelling
• Specify, deploy, verify and maintain computer-based systems

Talis Reading list

http://readinglists.bangor.ac.uk/modules/icl-2011.html

Reading list

Spiegelhalter , D., 2019. The Art of Statistics: Learning from Data. Pelican Books.

Williams, A., 2017. Data Analytics for Beginners: Introduction to Data Analytics. CreateSpace Independent Publishing Platform

Geary, D., 2012. Core HTML5 Canvas: Graphics, Animation, and Game Development. Prentice Hall

Cecco, R. 2011., Supercharged JavaScript Graphics: with HTML5 canvas, jQuery, and More. O'Reilly Media

Compulsory in courses:

• H116: BSc Applied Data Science (Degree Apprenticeship) year 2 (BSC/ADS)
• H120: BSc Applied Data Science (Degree Apprentice - Coleg Cambria) year 2 (BSC/ADSC)