Program Overview

Decision Making Data Analytics (BENV0166)

Key Information

• Faculty: UCL Bartlett Faculty of the Built Environment
• Teaching department: Bartlett School of Environment, Energy and Resources
• Credit value: 15
• Restrictions: This module is restricted to undergraduate BSEER students.

Alternative Credit Options

There are no alternative credit options available for this module.

Description

The module introduces students to mathematical methods for data-driven decision-making and optimization techniques to solve practical problems in the built environment domain. The course teaches an understanding of the mathematical foundations of optimization problems, formulating and solving real-world multi-objective problems using optimization and decision-making frameworks, developing computing and programming skills using Python, while emphasizing justifications for decisions and critical thinking.

The module content includes a number of different topics. We will start by introducing the fundamental approaches to optimizing single and multi-variable functions using analytical, Newton-Raphson, and Gradient Descent methods. Next, students will be introduced to multi-objective problems, how to define them in mathematical frameworks, and how to use techniques such as pareto front and weighted-sum approaches to find optimal solutions. The third part of the course will build on these foundational skills, culminating in a project focused on the optimization of a building design or similar problem from the built environment. Here, students will apply lessons from the topics of experimental design, parametric and sensitivity analysis, and optimization algorithms such as particle swarm and genetic algorithm.

Aims and Outcomes

Aims of the Module

• Equip students with analytical tools for formulating and solving decision-making problems.
• Develop practical and critical skills in data analysis, computing, simulation, and developing workflows independently.

By the End of the Module

• Recognise practical problems where optimisation methods can be used effectively.
• Implement appropriate decision-making algorithms and understand their procedures.
• Know when it is appropriate to apply a range of techniques, based on an understanding of their theoretical underpinnings.
• Implement models and algorithms using Python.

Module Deliveries for 2026/27 Academic Year

• Intended teaching term: Term 1
• Undergraduate (FHEQ Level 6)

Teaching and Assessment

• Mode of study: In person
• Methods of assessment:
  • 60% Coursework (2 assessments)
  • 40% Exam
• Mark scheme: Numeric Marks

Other Information

• Number of students on module in previous year: 30
• Module leader: Mr Michael Gutland

Last Updated

This module description was last updated on 10th March 2026.