Module Catalogue 2026/27

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

This module aims to equip students from diverse backgrounds with the fundamental mathematical, statistical, computational, and spatial analysis skills required to apply data science methods for advanced study and problem-solving in civil and geospatial engineering. Covering core mathematical concepts, numerical methods, programming in Python, and GIS-based spatial analysis, the module ensures that all students acquire the prerequisites to effectively manipulate equations, and undertake data science fundamentals including data analysis, developing mathematical models, and data visualisation.

Outline Of Syllabus

The module will cover a key range of fundamental concepts which are essential for learners and will be accessible to those from a range of diverse backgrounds and prior knowledge. Core mathematical foundations will be covered such as solving equations. The key data science principles will be introduced and then followed by an overview of key numerical and statistical methods and how they are applied in data science. Technical skills including computer programming will be introduced as a fundamental skill for data science and other tasks such as statistics, data processing and for solving large complex problems. The concept of Machine Learning (ML) as well as Artificial Intelligence (AI) will also be introduced. Knowledge and skills for the handling, processing, analysis and visualisation of spatial data will also be developed as key tool for the use of data in the built and natural environments.

Intended Knowledge Outcomes

On successful completion of this module, students will have the ability to analyse and interpret data using data science techniques and develop quantitative models using appropriate numerical techniques (M1, M2, M3). Specific knowledge will be gained on:

• Foundational statistical methods and processes for data handling for complex data analysis;
• Approaches for manipulating and solving complex equations such as integration and differentiation;
• Using programming and GIS as digital tools to perform data science, including data acquisition,
analysis, processing and visualisation.

Intended Skill Outcomes

On successful completion of this module, students will have developed skills in problem solving, numeracy, computer literacy & data analysis including spatial data (M2, M4). Specific skills gained will include the ability to:
• Manipulate and solve equations, formulae and functions using core mathematical approaches;
• Derive and create mathematical models;
• Conduct basic data analysis and statistics using software;
• Write programming code for information extraction, data handling and analysis and visualisation;
• Use digital software such as GIS for spatial data processing and analysis for spatial problems and
for spatial data visualisation.

Teaching Rationale And Relationship

The teaching methods in this module are designed to accommodate students from diverse backgrounds, ensuring they acquire the necessary foundational and advanced skills in mathematical, statistical, computational, and spatial analysis. The approach integrates lectures, hands-on practicals, problem-solving sessions, and project-based learning to align with the intended learning outcomes.

Assessment Rationale And Relationship

This module is assessed through coursework and quizzes, supported by a formative assessment. The assessment strategy is designed to reflect the applied nature of the module, focusing on real-world problem-solving using data analysis, programming, and geospatial methods. These components act as developmental milestones and support student confidence and competence in key technical areas. The portfolio element ensures foundational competencies are achieved in underpinning mathematical skills, while the larger report submission uses authentic assessment practice to assess skills in the application of data analysis and visualisation.

General Notes

N/A