Data Systems, Management & Eth
Run by School of Computer Science and Electronic Engineering
20.000 Credits or 10.000 ECTS Credits
Overall aims and purpose
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).
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.
Distinguish different classes of data.
Discuss the role of data and analytics in the 'data economy'.
Recognise potential legal and ethical consequences of collection, processing, and use of data.
Examine appropriate methods to collect and store data.
Teaching and Learning Strategy
Traditional lecture (1 hr x 12 weeks)
Tutorial for assistance with assessments (2 hrs x 12 weeks)
Private study, including completing assessments.
- 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
Subject specific skills
- 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;
- Demonstrate familiarity with relevant subject specific and general computer software packages.
- 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
Courses including this module
Compulsory in courses:
- N109: BSc Bus Analytics w Financial Tech year 2 (BSC/BAFT)
- N312: BSc Banking with Financial Tech year 2 (BSC/BKFT)
- H118: BSc Data Science & Artificial Intelligencetellig year 2 (BSC/DSAI)
- H113: BSc Data Science and Machine Learning year 2 (BSC/DSML)
- H114: BSc Data Science and Visualisation year 2 (BSC/DSV)
Optional in courses:
- G400: BSC Computer Science year 2 (BSC/CS)
- G40B: BSc Computer Science (4 year with Incorporated Foundation) year 2 (BSC/CS1)
- G40F: BSc Computer Science year 2 (BSC/CSF)
- I102: BSc Computer Science (with International Experience) year 2 (BSC/CSIE)
- G40P: BSc Computer Science with Industrial Placement year 2 (BSC/CSIP)
- H117: MComp Computer Science year 2 (MCOMP/CS)