In this era of unprecedented data and computational power, analytics is proving to be the cornerstone of ever-growing areas of organisational decision making, be it for example for businesses to compete or for public sector organisations to innovate. There are several aspects in applying analytics to real business and decision-making scenarios and problems. These not only include computational and algorithmic methodologies, which encompass three main strands of analytics (descriptive, predictive and prescriptive) but also problem structuring methodologies like SSM. A key ability expected of an analyst or analytics practitioner is to have a good idea and understanding of how these aspects of analytic decision-making play together in any given context. Hence, the aim of this module is to enable students to develop analytic thinking at a holistic level to make informed choices when using or recommending analytics in a real-world context.

After introducing the topic of forecasting in business organisations, issues concerned with forecasting model building in regression and its extensions are presented, building on material covered earlier in the course(s). Extrapolative forecasting methods, in particular Exponential Smoothing, are then considered, as well as Machine Learning / Artificial Intelligence methods, in particular Neural Networks. All methods are embedded in a case study in forecasting in organisations. The course ends by analysing how forecasting is applied to operations and how forecasting can best be improved in an organisational context.

This module provides an introduction to business analytics, management science and related disciplines, with a particular emphasis on the modelling and solution of real-world problems. It includes an introduction to three very useful approaches: optimisation, stochastic modelling and simulation.Several examples and case studies will be given to illustrate how the approaches can be used in practice. Students will also be shown how to implement models in a spreadsheet. Some hands-on experience with Excel, including the development of simple macros, will be given.

Python is a simple, yet very powerful, high-level computer programming language that is extremely popular today. It is widely used in many scientific areas for data exploration whilst being the preferred programming language in a range of modern organisations.

This module will cover a range of the most commonly used algorithms and techniques, including sorting and searching algorithms, data manipulation, visualisation and the use of package extensions such as pandas and numpty, as well as applications in Business Analytics such as simulation and optimisation.

At the heart of many management problems lies data that need to be described, analysed and interpreted. The overall objective of this module is to develop the students’ abilities to describe, analyse and interpret data soundly, making effective use of computer software. The skills learned on this course underpin many of the other modules on the Management Science Department programmes. Although the module assumes little prior knowledge of statistics, those with prior knowledge should not expect to find it easy.

The aim of this module is to provide you with a valuable opportunity to research a real business problem for an organisation, delivering a viable and well-documented solution to the client. For a successful project, you will employ your knowledge of methods (qualitative/quantitative), problem scoping and the research literature to your knowledge and understanding of professional engagement both in their interactions with the client and in the reporting of their outputs and discourse on your research.

The aim of this module is to provide students with a valuable opportunity to research an area of your degree programme in-depth, linking this to business problems that arise in the real world. For a successful project, you will employ their knowledge of analytical methods, problem scoping, modelling and the research literature to identify and implement a solution approach to the business problem or expand on your knowledge of the state of the area being researched. Throughout the project, you will develop your knowledge and understanding of professional academic engagement both in your interactions with your supervisor and in the reporting of your outputs and discourse on your research.

The course provides an introduction to the fundamental methods and approaches from the interrelated areas of data mining, statistical/ machine learning, and intelligent data analysis. The course covers the entire data analysis process, starting from the formulation of a project objective, developing an understanding of the available data and other resources, up to the point of statistical modelling and performance assessment. The focus of the course is classification and uses the R programming language.

This module introduces you to the basic principles of marketing and places particular emphasis on the role of analytical modelling in supporting marketing decisions in organisations. It is hoped that you will be in a position at the end of this course to fully appreciate how analytical models enable managers to make better marketing decisions instead of relying on their intuition or gut feel.

Topics covered include Marketing Mix Models (MMM) and text mining and are supported by workshops, which will offer opportunities to build up modelling skills. These will be done in R (or equivalently other open-source statistical programming languages, such as Python).

The Operations Management discipline has traditionally been concerned with how organisations achieve excellence in processes and operations, and to this end several (continuous) improvement approaches and tools and techniques have been stressed. The interest in intra-firm improvement and excellence has more recently been extended to address also how firms can improve their operations in coordination with their supply chain counterparts. This has become relevant since specialisation of labour across industries and outsourcing trends entail that a large share of the value of products and services is created outside the focal firm.

This module provides an understanding of both intra- and inter-organisational approaches to operations improvement and excellence. In particular, it address: a) improvement tools and techniques that firms implement primarily in their internal operations, and b) supply processes and capabilities required to improve operations and achieve excellence in coordination with supply chain partners. The themes above are addressed both in manufacturing and service industries and cross private and public sector settings.

Optimisation, sometimes called mathematical programming, has applications in many fields, including operational research, computer science, statistics, finance, engineering and the physical sciences. Commercial optimisation software is now capable of solving many industrial-scale problems to proven optimality.

The module is designed to enable students to apply optimisation techniques to business problems. Building on the introduction to optimisation in the first term, students will be introduced to different problem formulations and algorithmic methods to guide decision making in business and other organisations.

Computer simulation methods are among the most commonly used approaches within Operational Research. Modern simulation packages are a valuable aid in building a simulation model and this module will emphasise the practical application of simulation, with a good understanding of how a simulation model works being an essential part of this. Stochastic modelling methods provide analytical tools which enable Operational Researchers to gain insight into complicated and unpredictable real-world processes. The application of these methods requires careful consideration of the dynamics of the real-world situation being modelled, and (in particular) the way that uncertainty evolves.

Simulation and stochastic modelling are inter-related in several ways. Insights from stochastic modelling can help in the design of simulation models. On the other hand, simulation can be used to investigate the sensitivity of stochastic models to some of their underlying assumptions. A number of fundamental concepts, such as probabilistic variations, steady state behaviour and time-dependent behaviour are common to both areas.

The purpose of this course is to understand and use mathematical models in making strategic, tactical, and operational logistics decisions. Emerging logistical concepts will be introduced and the associated mathematical modelling needs will be discussed. Algebraic formulations will be used as vehicle for describing models and discussing their relationships. There will be a focus on modelling, the use of professional software, and the understanding of results. For problems where exact solutions are hard to achieve even for simple instances of the problem, heuristics will be discussed. The main topics covered are: facility location, network design, warehousing, vehicle routing and scheduling, and Terminal (airport) capacity management.

Depending on students need and level of programming skill, the computer workshops will focus on either solver languages (e.g. GAMS, AMPL, MPL) and/or programming interfaces (PYOMO, CPLEX Concert, Gurobi Python Interface).