Rhedir gan School of Computer Science and Electronic Engineering

10.000 Credyd neu 5.000 Credyd ECTS

Semester 1

This technological age allows information to be shared more frequently and more freely. This has made the subject of Data Ethics more important than ever before.

This module provides a framework to analyse these concerns as the students examine the ethical and privacy implications of collecting and managing 'big data'.

The topics that the students will study may include;

• What is data ethics and how is data used? – What is ‘Big Data’? How do we use ‘Big Data’? How do we analyse and extract information from ‘Big Data’?
• What is privacy? - Data privacy or information privacy is a branch of data security concerned with the proper handling of data – consent, notice, and regulatory obligations
• Relevant aspects of the law – Data Protection Act, Freedom of Information Act
• Public consent – What is GDPR? Why is it important? What to regulate. How much understanding does the public have about consent?
• What are ethical best practices for data practitioners? eg The Data Ethics group led by the Alan Turing Institute.
• The Data Ethics Framework principles – Government guidance, the principles

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. Identify the potential ethical harms and benefits presented by data

2. Identify and articulate some basic ethical and policy-based frameworks

3. Understand Ethical ‘best practices’ for data practitioners

4. Describe the relationship between data, ethics, and society

Math	Enw	Disgrifiad	Pwysau
	Case Study Report		70.00
	Personal Data Presentation		30.00

		Oriau
Lecture	The classroom-based element will include student-centred learning methods such as interactive lectures, case studies, group discussions and practical workshops.	30
Private study	The tutor directed student learning will be supported by online learning materials hosted or signposted on the VLE.	70

• 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
• Hunanymwybyddiaeth & Ystyried - Bod yn ymwybodol o'ch cryfderau, gwendidau, nodau ac amcanion eich hun. Gallu adolygu ,cloriannu a myfyrio'n rheolaidd ar eich perfformiad eich hun ac eraill.

• Knowledge and understanding of facts, concepts, principles & theories
• Recognise legal, social, ethical & professional issues
• Knowledge and understanding of commercial and economic issues
• Knowledge of management techniques to achieve objectives
• Knowledge of information security issues
• Evaluate systems in terms of quality and trade-offs
• Development of general transferable skills
• Deploy systems to meet business goals
• Knowledge of systems architecture
• Knowledge and understanding of computational modelling

Gorfodol mewn cyrsiau:

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