Module manager: Emilio Garcia-Taengua
Email: E.Garcia-Taengua@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
Admission to UG programmes in the School of Civil Engineering
This module is not approved as a discovery module
This module provides students with the knowledge of data science methods and the necessary skills to apply them to research and development in engineering. Enhanced data literacy is highly valued in industry and very useful for the design of research projects. Students taking this module will develop a data analytics way of thinking and learn about data pre-processing and mining, machine learning, multivariate statistics, design of experiments, and methods of qualitative analysis.
The objectives of this module are:
-To understand the principles of doing research with data and the importance of detailed ‘planning before doing’ in research projects.
-To understand how to collect, organise and prepare data in a structured and systematic way.
-To learn how to use descriptive statistics, correlation coefficients and visualisations for exploratory analysis and detection of outliers.
-To acquire the knowledge and skills required for analysing and modelling multivariate information using linear models.
-To understand Design of Experiments (DOE), its advantages as well as its limitations and how to address them.
-To learn the principles of dimensionality reduction techniques and in what circumstances they can be useful.
-To understand the fundamentals of qualitative research and how to work with non-quantitative data.
On successful completion of the module, students will have demonstrated the following learning outcomes:
1. Apply a comprehensive knowledge of mathematics, statistics and engineering principles to the analysis of quantitative and non-quantitative data.
2. Formulate and exploit statistical models to extract information from data and generate knowledge.
3. Select and apply different methods and techniques to structure and analyse information, discussing the limitations of the techniques employed.
4. Design experimental programmes and data collection plans that are effective and efficient means to generate data that can be subject to robust and conclusive analysis.
5. Communicate information and describe complex sets of data by selecting and appropriately using a variety of visualisation techniques.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills :
a. Problem solving and analytical skills
b. Time management , planning and organising
c. Reliability or reproducibility of data
d. Interpretation of data
e. Writing reports /communication
-Fundamentals: the scientific method, quantitative and qualitative research, research questions in engineering, machine learning and AI.
-The multivariate context: variables and observations, observational vs controlled data, linear and non-linear relationships, interactions, association, inference, causality.
-Collecting data: organisation and structuring, univariate descriptive analysis, outliers and data ‘cleaning’, variable transformations and when to use them, software available.
-Evaluating differences in datasets: moving on from t-tests to analysis of variance (ANOVA), p-values, practical tips.
-Supervised learning methods based on linear models: multiple linear regression, how to specify a good model, goodness of fit measures, overfitting and the accuracy paradox, visualisation using the response surface method.
-Dimensionality reduction: appropriateness and advantages, Principal Component Analysis (PCA), factor extraction.
-Introduction to qualitative research methods: design of qualitative research projects, sampling, types of interviews for data collection, observation and positionality, document analysis, software available.
Methods of assessment
The assessment details for this module will be provided at the start of the academic year
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 20 | 1 | 20 |
| Practical | 10 | 1 | 10 |
| Independent online learning hours | 10 | ||
| Private study hours | 60 | ||
| Total Contact hours | 30 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Students will have to complete a number of tasks on different sets of data provided during the first week of the module. In the practical sessions, they will receive formative feedback on their work.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team