Rhedir gan School of Computer Science and Electronic Engineering

20.000 Credyd neu 10.000 Credyd ECTS

Semester 1

Trefnydd: Dr Cameron Gray

This module aims to:

• Provide students with an environment to experiment with techniques taught
• Examine non-trivial data sets.

The format of this module is a free-form project where students are able to experiement with the techniques, methods, and processes taught and apply them to a student-selected data set. Tutors will advise on suitability of these data sets to ensure the required complexity and dimensionality.

Indicative content includes:

• Data analysis methods and algorithms.
• OSEMN (Obtain, Scrun, Explore, Model, iNterpret) cycle.
• Methods for reporting data science experiments.
• Applying acquired knowledge and skills to a student-selected data set or sets.

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.

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.

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.

1. Employ data science techniques with a data-set.

2. Report results of experiments analysing data.

3. Evaluate the effacacy of experiments conducted.

Math	Enw	Disgrifiad	Pwysau
	Project		100.00

		Oriau
Tutorial	Tutorials on a per-student as needed basis.	4
Private study	Experimentation and write up of the project portfolio.	196

• 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
• Dadl - Gallu cyflwyno, trafod a chyfiawnhau barn neu lwybr gweithredu, naill ai gydag unigolyn neu mewn grwˆp ehangach

• Deploy tools effectively
• Development of general transferable skills
• Deploy systems to meet business goals
• Defining problems, managing design process and evaluating outcomes
• 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
• Principles of appropriate supporting engineering and scientific disciplines

Gorfodol mewn cyrsiau:

• G5BB: MSc Advanced Data Science year 1 (MSC/ADS)