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Modules

Programme Structure

Our course finder pages contain all the most up-to-date information about the Business Analytics MSc, including details of the programme structure, compulsory and elective modules and study options.

Click here to see the full MSc Business Analytics programme structure

Below is a full list of all modules which are expected to be available to students on this programme across the semesters.  Please note that this is for information only and may be subject to change. Click the link above for accurate information about which of these modules are compulsory and elective for each semester of your MSc programme.

Modules with codes beginning MTH are taught by the School of Mathematical Sciences (SMS).  Modules with codes beginning BUS are taught by the School of Business and Management (SBM).  Modules are assessed by a mixture of coursework, in-term assessment and final examinations, with examinations being held between late April and early June.


Data Analytics refers to the use of statistics and machine learning in inferring information from data sets, with the ultimate goal of gaining insight and aiding decision-making.

This compulsory module provides statistical foundations for data modelling, collection, and analysis, and introduces key techniques in statistical machine learning like regression, classification, principal component analysis, and clustering. Tutorials focus on the use of the R software environment in the analysis of real-world data.

This module will equip you with the skills needed to assess and create an entrepreneurial venture.  It combines rigorous entrepreneurship theory and concepts with practical applications, e.g. business plan writing. The focus on practical skills adds a crucial facet to the programme by providing the skills required to structure an independent venture. Throughout the module, students will engage in a practical entrepreneurial learning exercise that will lead to the creation of a business plan. This activity will be supported by seminar sessions, individual mentoring and the use of business planning software in tutorials.


The aim of the module is to introduce students to the problem of causal inference, to theories of how causality is established and to empirical methods used to identify causal effects. The main focus will be on randomised controlled trials and settings that are similar. You will learn about different econometric techniques used to identify causal effects and will develop an understanding of the strengths and weaknesses of these effects. You will also learn how to collect and organise data that comes from real or natural experiments, to analyse such data and to report on your results in ways that are accessible to non-specialists.


The group project gives you the opportunity to work with other students to provide analysis of a problem or question using complex data from a business context. Each group will be assigned a mentor who will guide them through the process of structuring the analytical problem, obtaining and organising the data, data analysis and presentation of results.

As part of this module groups will present initial results to an audience consisting of mentors and practitioners. Final assessment of the module will then be based on individual essays which cover specific aspects of the case and in which you will be required to reflect on your work in the light of the methods and theories which your learning in the MSc has touched upon.


The primary aim of this module is to help you develop a mix of leadership skills necessary to provide effective leadership for analytical initiatives and projects. You will get an understanding of how to lead a team with transactional and transformational leadership which includes for example delegating the 'right' tasks, inspiring others, persuasive communication, and stimulating team members to think outside the box. You will also develop the capacity to work effectively beyond the borders of your team and to build high-quality relationships with peers, other team leaders and stakeholders. Finally, you will be equipped with skills necessary to create buy-in from internal and external clients such as presentations skills, creating convincing narratives and pitching initiatives.


The module will be split across two terms. There will be six sessions in each term at which outside speakers will present cases on business analytics in companies or cases on the context of business analytics in society or a specific business analytics tool. Each case study will be preceded by student self study on the main topics and will be followed by a seminar in which students summarise insights and the case study. The course will be assessed through students' presentations and through individual coursework in which students elaborate on one of the topics presented by the outside speakers.


Optimisation refers to the selection of the best alternative, according to some criterion, from a set of available alternatives.

This module introduces standard models from mathematical optimisation, like network flows and linear programmes, and their use in solving real-world optimisation problems; in staff and project scheduling, commodity trading, production, and sales. Tutorials focus on modelling of real-world optimisation problems based on data, and on the use of software such as R, Excel, and Gurobi to solve optimisation problems and make better decisions.


In business environments the ability to use key software packages is vital, particularly the universally used Microsoft Office portfolio. This module will teach you how to customise and program two key aspects of Microsoft Office used in Analytics; the database package Access and the spreadsheet software Excel.

You will learn Visual Basic for Applications (VBA), the most prevalent programming language in industry and some Structured Query Language (SQL) for data manipulation. The course is taught by an actuary with 15 years industry experience in this area.


The focus of this module will be on recent project management techniques that encourage the use of incremental delivery of projects. These techniques are appropriate to projects that deliver complex outcomes in a context of high uncertainty about the desired result. The course will also provide a grounding in traditional project management techniques that focus on projects that are concluded to a clear specification within a pre-specified time frame.

You will be encouraged to take advantage of opportunities to earn an accreditation for project management and the course will prepare you for this additional examination.


This is a compulsory module for students wishing to further their understanding of the visualisation techniques used in business decision processes using the powerful SAS Visual Analytics software.

You will apply the power of SAS analytics to massive amounts of data, gain valuable insights into visualisation techniques to uncover relevant patterns, and be empowered to make quicker informed decisions.


This module will explore various theoretical approaches used to explain what markets managers choose to compete within, why and how. We will begin by examining the "traditional" competitive positioning and resource-based views, and critically evaluate these analytical approaches and their appropriateness in an increasingly networked, globalised, digitised and fluid competitive environment. We will then go on to examine the challenges of strategy implementation, including analysing structural, cultural and functional issues.


Time Series Analysis refers to the use of statistical and machine learning methods for inference on datasets containing variables collected over time, with the ultimate goal of forecasting the values of these variables at some future time.

This module introduces key concepts such as trend and seasonality decomposition, autocorrelation, autoregressive and moving average models, and exponential methods. Tutorials focus on the use of the R software environment in the analysis of real-world time series data.

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