Introductory description

Analytics Engineering, AI and Visualisation provides participants with a theoretical and practical exposure to modern data workflows in business analytics and AI. Covering all aspects of an organisation's data lifecycle, from data extraction through to product deployment (in the form of business dashboards), the module not only provides exposure to a critical element of business analytics and AI practice, but also provides foundational concepts on which later modules in the course will build upon.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The syllabus covers:

• Data models and data architectures;
• Cloud technologies;
• Data engineering and analytics engineering;
• AI systems and non-traditional data;
• Data visualisation and dashboards;

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate a critical and practical understanding of analytics engineering concepts such as data pipelines, data transformation and data processing.
• Critically evaluate different data models and architectures and determine appropriate designs for different business cases.
• Evaluate business cases and requirements for opportunities to apply analytics engineering concepts.

Indicative reading list

• Belorkar, A. et al. (2020) Interactive data visualization with Python: present your data as an effective and compelling story. Second edition. Birmingham, UK: Packt.
• Huyen, C. (2025). AI Engineering: Building applications with foundation models. Sebastapol, CA: O'Reilly.
• Kleppman, M. (2017). Designing Data Intensive Applications. Sebastapol, CA: O'Reilly.
• Mayer-Schönberger, V. and Cukier, K. (2014) Big data: a revolution that will transform how we live, work, and think. Boston: Mariner Books.
• McCandless, D. (2012) Information is Beautiful (New Edition). London: HarperCollins Publishers.
• Pivert, O. (ed.) (2018) NoSQL data models: trends and challenges. London, UK: ISTE.
• Reis, J. and Housley, M. (2022). Fundamentals of Data Engineering: Plan and build robust data systems. Sebastapol, CA: O'Reilly.

Subject specific skills

Apply best practice principles of visual communication and dashboard design when creating databases and data architectures.
Design efficient and effective data transformations and pipelines.
Effectively use visualisation and dashboard software.
Select and communicate complex technology choices and data insights to a range of audiences.

Transferable skills

Computing skills.
Data presentation skills, literacy and practices.
Problem solving skills