Compulsory modules

Research and Study Methods (10 credits)

This module will equip you with the skills necessary to perform research and employ effective study methods which will underpin your dissertation.

Data Science Foundations (10 credits)

This module presents an overview of core data science concepts and tools, focusing on real-life data science research questions with practical exposure to R and/ or Python programming as an integral part of the course.

Survey Fundamentals (10 credits)

This module provides an overview of sampling and estimation fundamentals.

Statistical Programming (10 credits)

This module introduces core programming techniques in R essential for performing data manipulation, data processing and data analyses of traditional and alternative data sources through practical sessions.

Introduction to Survey Research (10 credits)

This module introduces the stages involved with planning and undertaking surveys. It will consider the methodological issues that may arise, including errors, and will discuss options for minimising the impact through the survey design.

Regression Modelling (10 credits)

This module will introduce the basic regression model - residual analysis, model building and selection, and the handling of categorical variables. Also, Logistic regression (binary response regression) will be introduced, assessing the model fit and model building and selection. Finally, Multiple regression and Multivariate regression modelling will be introduced.

Advanced Statistical Modelling (10 credits)

This module introduces a broad class of linear and nonlinear statistical models and the principles of likelihood inference to a variety of commonly encountered data analysis problems in variety of disciplines.

Time Series Analysis (10 credits)

This module introduces you to time series and forecasting methods.

Introduction to Machine Learning (10 credits)

This module provides you with the principles of computer learning and its applications. It covers the fundamentals of machine learning methodologies, implementations and analysis methods appropriate for machine learning applications.

Advanced Machine Learning (10 credits)

This module builds on the Intro to Machine Learning module. It focuses on Advanced Programming Skills and Neural Computing as an extension of machine learning, natural language processing & multi-media. It considers supervised and unsupervised machine learning algorithms (random forests, neural networks, clustering, Log regression, and support vector machines) alongside more advanced Imaging and multi-media data processing.

Introduction to Distributed Systems (10 credits)

This module provides an overview of processing data at large scale and parallel processing. It introduces Hadoop and Spark and the use of parallel processing paradigms.

Data Visualisation (10 credits)

This module builds on the basic data visualisations introduced in the compulsory modules. It will cover information design, interaction design and user engagement; state of the art tools to build useful visualisations for different types of data sets and application scenarios.

Final Project

Compulsory modules
Dissertation in Data Analytics (60 credits)

Students on the MSc are also required to pass complete a dissertation on a data science focussed topic related to their programme of study.

The exact content of each dissertation will vary in accordance to the title but will involve you completing a literature review and research of the topic at an advanced level, the preparation of a project proposal, the application of analytical techniques and academic approaches to the generation of alternative solutions and synthesis of a solution for the complex problem in hand, together with the presentation of the solution in oral and written form.


Learning and teaching

Our course has a supportive teaching and learning strategy based on active student engagement.

We use a variety of teaching and assessment methods such as:

 critical appraisal reports
 data analysis reports
 data analysis using software applications
 presentations and case studies.

Learning methods include:

 blended learning
 formal lectures
 problem solving practicals
 guided independent learning
 use of the computer based virtual learning environment ‘Moodle’
 independent research
 software data analyses
 experiments.