Rhedir gan School of Computer Science and Electronic Engineering

10.000 Credydau neu 5.000 Credyd ECTS

Semester1

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.

dda

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.

rhagorol

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

• 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
• Exploring - Able to investigate, research and consider alternatives
• Information retrieval - Able to access different and multiple sources of information
• 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.
• Argument - Able to put forward, debate and justify an opinion or a course of action, with an individual or in a wider group setting
• Self-awareness & Reflectivity - Having an awareness of your own strengths, weaknesses, aims and objectives. Able to regularly review, evaluate and reflect upon the performance of yourself and others

• 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)