Run by School of Computer Science and Electronic Engineering

20.000 Credits or 10.000 ECTS Credits

Semester 1 & 2

This module will provide the learners with the skills required to analyse data sets. The skills gained will help the learners go through data preparation, data pre-processing, data analysis and post processing. A range of tools will be used in this module. The module is designed to look at how data is collected, the quality of the data and data cleaning. Furthermore, it is designed to allow the learners to use analysis techniques and process the data using a variety of tools .

Indicative content includes:

• Data pre-processing – Looking at how data mining techniques are involved with cleaning data, improving the quality of data and the preparation of data, to make it meaningful.
• Data mining – Looking at how machine learning techniques, such as supervised machine learning and unsupervised machine learning are used to develop programs without the need for instructions.
• Data Analysis – Writing a bespoke programme to analyse a given data set using industry standard packages (e.g. NumPy or SciPy).
• Data Evaluation – Interpreting your data to test your programme outcomes.

threshold

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.

good

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.

excellent

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. Employ appropriate analytical tools to conduct data analysis.

2. Illustrate the core concepts and process of data analytics.

3. Evaluate methods and techniques used in data analytics.

4. Evaluate results from the use of data analytical tools.

Type	Name	Description	Weight
	Individual Report		25.00
	Analysis Programme		50.00
	Evaluative Report		25.00

		Hours
Practical classes and workshops	Classroom-based element will include student-centred learning methods such as interactive lectures, case studies, group discussions and practical workshops.	60
Private study	Tutor-directed student learning will be supported by online learning materials hosted or signposted on the Grŵp VLE.	140

• 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
• 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.

• Knowledge and understanding of facts, concepts, principles & theories
• Use of such knowledge in modelling and design
• Problem solving strategies
• Analyse if/how a system meets current and future requirements
• Deploy theory in design, implementation and evaluation of systems
• Knowledge of management techniques to achieve objectives
• Specify, design or construct computer-based systems
• Evaluate systems in terms of quality and trade-offs
• Deploy tools effectively
• Development of general transferable skills
• Defining problems, managing design process and evaluating outcomes
• Knowledge and understanding of mathematical principles
• Knowledge and understanding of computational modelling
• Principles of appropriate supporting engineering and scientific disciplines

Compulsory in courses:

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