1. Demonstrate through critical appraisal key data science ideas, including techniques, methods, and use cases (and be able to apply tools appropriately in solving related problems) (AHEP 4: M2, M3, M5)

Knowledge and Understanding, Problem Solving, Application

2. Critically evaluate data mining approaches in the context of Business data and select the most appropriate tools and techniques for analysis of complex issues (AHEP 4: M2, M3)

Analysis,
Reflection

3. Demonstrate comprehensive understanding of themes related to statistical analysis and Machine Learning applied to complex issues and problems (AHEP 4: M1)

Learning, Application

4. Develop critical comprehension of large-scale data management and stream processing themes used commercially (AHEP 4: M2, M5)

Communication, Learning

Written Report - A management style report weighted at 50% based on a scenario which identifies the issues involved in data harvesting and data modelling and which makes recommendations on the use of these areas in the context of the scenario (Learning Outcomes 1 and 2). Meeting AHEP 4 Outcomes: M2, M3, M5.



Practical Examination – A practical examination weighted at 50% based on an open book case study. Students will be given a business case study for which they need to do the following tasks. Firstly they will review the case study to draw a clear understanding of it. Secondly they will need to work through and apply key data science approaches to it. Finally they must create workable solutions to problems found within it. The examination will therefore at depth include a coverage of analysis, design and practical implementation through an appropriate use of tools in finding solutions to problems presented in the case study scenario. It will also address statistical and Machine Learning roles in the practical aspects of the case study and how the student’s final solution considers commercial aspects (Learning Outcomes 3 and 4). Meeting AHEP 4 Outcomes: M1, M2, M5.



Professional Body requirements mean that a minimum overall score of 50% is required to pass a module, with each element of assessment requiring a minimum mark of 40%.

The module will address Data Science and will cover essential themes, case studies, business examples, and terminology in approach. The module will look at practical tools and a contrast of their functions. More specifically content will address an examination of application examples and cutting-edge technologies used in the commercial environment, including advanced data gathering techniques (sampling and crawling), approaches for data curation and quality assurance, assessment frameworks, advanced statistical analysis, manual and crowdsourcing techniques, advanced analytics of data (statistical modelling, basic concepts, experiment design, and common pitfalls), complex use of interpreting data (visualization techniques, and pitfalls), advanced maths and arithmetic, uses of Machine Learning, overview of data related themes, and OLAP approaches and technologies (Online Analytical Processing).



There will be an introduction to Business Data Analytics, the Data Analytics Lifecycle, Data modelling, Clustering, Association Rules, Regression, Classification, Time Series Analysis, Text Analysis, Data visualisation techniques, Data structures required to support data mining – e.g. weka files, relational, and noSQL etc., Concepts of Data Warehousing, Data Warehousing design approaches, Architectures, schemas, tuning, loading, etc., Dimensional modelling, Performance issues, Kimball and Inmon, Data lakes, Issues of data cleansing, Data governance, Data integration, Related security issues, Data transformation, Working with structured and unstructured data, and Using Data Warehousing techniques with a relational database.


As an end topic the module will focus on Computer vision and components that will include: Image representation, filters, texture, colour, Multiview geometry, shapes, segmentation and clustering. There will be modelling techniques and approaches to design trade-offs and efficiencies. As a final focus there will be topics of face detection, and components of 3D vision.

This module will focus on data science providing an extensive examination of its related components which includes mining, informatics, and visualisation. It will focus on infrastructure so that Business Intelligence and Data Analytics can be successfully deployed to improve an organisation’s ability to gain knowledge from its existing databases. The module will specifically focus on concepts and technologies such as Data Warehousing and the techniques of handling data. Within the modules assessments students will look at case study examples to design and implement solutions around data harvesting and mining.

Theory will be delivered via lectures and supported by practical classes, seminars and discussion groups. In addition, you will be provided with a range of resources for independent study such as case studies, academic papers and industry stories. There will be a mixture of practical and theoretical formative exercises which will help you build your knowledge and confidence as well as preparing you for the assessment.

Wu, X. Spiliopoulou, M., Wang, C. et. al. (2025), Data Science: Foundations and Applications: 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, Australia, Springer

Douglas, C., Montgomery, S. E. Fricker, R. D. (2025), Introduction to Probability and Statistics for Data Science: with R, Cambridge University Press

Gilbert, S. (2022), Good Data: An Optimist's Guide to Our Digital Future, Welbeck
Szeliski, R. (2022) Computer Vision: Algorithms and Applications (Texts in Computer Science), 2nd edition, Springer

Pancham, S (2023) Computer Vision: Applications of Visual AI and Image Processing: 15 (De Gruyter Frontiers in Computational Intelligence, 15), De Gruyter Press

Open-source data analysis tools