Rhedir gan School of Computer Science and Electronic Engineering

20.000 Credyd neu 10.000 Credyd ECTS

Semester 1

Trefnydd: Dr Cameron Gray

The module aims to provide students with:

• an awareness of the DIKW (Data, Information, Knowledge, Wisdom) pyramid.
• understanding of classes of data and the processes that could be performed on it.
• an appreciation of the ethical issues surround processing of data.
• knowledge of the legal aspects when processing data.

Indicative content includes:

• Data; the definitions and concepts.
• Contextualisation of data.
• Analytical methods using data.
• Ethics of processing data with and without automatic decisions.
• Data Protection legislation (DPA, GDPR, HIPAA, COPPA, etc.).
• Data collection mechanisms.
• Storage mechanisms for data (in broad terms).

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.

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.

trothwy

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.

1. Examine appropriate methods to collect and store data.

2. Distinguish different classes of data.

3. Measure the role of data and analytics in the 'data economy'.

4. Appraise potential legal and ethical consequences of collection, processing, and use of data.

Math	Enw	Disgrifiad	Pwysau
	Class Test - Analysis and Storage Ethics		25.00
	Class Test - Data Categories		25.00
	Data Collection and Analysis Proposal		50.00

		Oriau
Lecture	Traditional lecture (1 hr x 12 weeks)	12
Tutorial	Tutorial for assistance with assessments (2 hrs x 12 weeks)	24
Private study	Private study, including completing assessments.	164

• Llythrennedd - Medrusrwydd mewn darllen ac ysgrifennu drwy amrywiaeth o gyfryngau
• Rhifedd - Medrusrwydd wrth ddefnyddio rhifau ar lefelau priodol o gywirdeb
• Defnyddio cyfrifiaduron - Medrusrwydd wrth ddefnyddio ystod o feddalwedd cyfrifiadurol
• 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
• Adalw gwybodaeth - Gallu mynd at wahanol ac amrywiol ffynonellau gwybodaeth
• 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.
• Dadl - Gallu cyflwyno, trafod a chyfiawnhau barn neu lwybr gweithredu, naill ai gydag unigolyn neu mewn grwˆp ehangach

• Solve problems logically and systematically;
• Appreciate the importance of designing products with due regard to good laboratory practice, health and safety considerations and ethical issues.
• Use both verbal and written communication skills to different target audiences;
• Knowledge and understanding of facts, concepts, principles & theories
• Use of such knowledge in modelling and design
• Problem solving strategies
• Deploy theory in design, implementation and evaluation of systems
• Recognise legal, social, ethical & professional issues
• Knowledge and understanding of commercial and economic issues
• Evaluate systems in terms of quality and trade-offs
• Development of general transferable skills
• Methods, techniques and tools for information modelling, management and security
• Specify, deploy, verify and maintain information systems
• Knowledge and/or understanding of appropriate scientific and engineering principles
• Knowledge and understanding of mathematical principles
• Knowledge and understanding of computational modelling
• Principles of appropriate supporting engineering and scientific disciplines

Gorfodol mewn cyrsiau:

• N2BC: MBA Information Management (with Incorporated Pre-Masters) year 1 (MBA/INFM1)
• N2BG: MBA Information Management (January start) year 1 (MBA/INFMANJ)
• N2AS: MBA Information Management year 1 (MBA/INFOMAN)
• H117: MComp Computer Science year 4 (MCOMP/CS)
• G4AS: MSc Advanced Computer Science year 1 (MSC/ACS)
• G5BB: MSc Advanced Data Science year 1 (MSC/ADS)
• G5BC: MSc Computing for Data Science year 1 (MSC/CDS)
• G5BA: MSc Computing year 1 (MSC/COMP)
• G5BD: MSc Rise of the Machines year 1 (MSC/MACH)