³Ô¹ÏºÚÁÏ

July-September

Before the start of your programme, you will be expected to complete online pre-study modules, delivered via Canvas, our virtual learning environment (VLE). Some of these modules are compulsory while others are strongly recommended as they will help you prepare you for the remainder of your programme.

Early September, compulsory

Your MSc Business Analytics & AI induction will be spent at our vibrant South Kensington campus in London. You will be invited to a welcome event where you can meet and network with your fellow students, distinguished faculty and dedicated Programme team, building new connections and getting a taste of life at ³Ô¹ÏºÚÁÏ.

This module introduces you to the design of algorithms and data structures for computational problem solving. The design of efficient computational methods for sifting through large data sets lies in the core of modern technological innovation, ranging from search engines and social networks to healthcare, energy and finance. The module will familiarise you with key algorithm design paradigms and central concepts of computational complexity and running time analysis. 

You will develop a working knowledge of basic algorithms (for problems such as search, sorting and shortest paths) and data structures, along with the necessary programming constructs. The module will also introduce you to the Python programming language and support you to become proficient in organising and writing programs for practical problem-solving.

This module introduces practical data wrangling and visualisation techniques in R and Tableau, emphasising how to ask insightful business questions and analyse data interactively. Exploratory data analysis methods will be discussed from a statistical point of view and with cases in a management context. Our discussions will be complemented by real datasets from retail, supply chain, finance and other industries. You will be prepared to use analytical and data-driven approaches to make informed management decisions.

The module provides a self-contained overview of deep learning and generative AI. The contents of the module consist of two parts. In the first part you will study the general framework of loss function-based machine learning as the cornerstone of all the modern deep learning models. You will connect the training of a machine learning model with optimisation algorithms of gradient descent and stochastic gradient descent. You will understand how the framework covers both the simple linear regression model and the complex neural network model as special cases, and discuss the specific training algorithms for fully-connected and convolutional neural networks. 

In the second part you will focus on understanding the generative AI models (such as large language models and multi-modal large language models) as machine learning models and showcase the state-of-the-art applications of these models on search, coding, text processing, etc. In particular, you will understand how the generative AI model platforms such as ChatGPT and Claude can be used for interactive data analytics and business analytics. 

This module introduces you to practical usage of databases with the main emphasis on SQL and related technologies. As big data problems are increasingly prevalent for business, this module introduces basics of their processing with Apache Spark – a versatile, big data processing engine. This module will use PostgreSQL, one of the most popular and powerful object-relational database management systems.

This module will teach you fundamentals of machine learning. It will focus on the most important concepts and algorithms, and review their practical use to solve real world problems. The goal of the module is to enable you to be data-savvy, analytically minded, and coding-literate problem solvers. 

This rigorous module will teach you concepts of probability, statistics and linear algebra. You will learn to summarise and analyse data using statistical concepts and understand linear algebra to represent and manipulate data. This will ensure that you build a solid foundation of the mathematical concepts necessary to succeed on the programme. This module will also serve as an introduction to R.

Optimisation plays a key role in statistical, machine learning and business operational models. This module trains students on the fundamental optimisation tools available to the modeller and business analyst. You will obtain hands-on experience with the key modelling (AIMMS, GAMS, AMPL) and optimisation (CPLEX, GUROBI) software packages.

This module covers the statistical tools needed to analyse data in economics and management. It focuses on parameter estimation and hypothesis testing within the framework of the classical linear regression model. Advanced topics include panel data methods and binary dependent variable models will also be discussed. Considerable attention is devoted to applications of the techniques in practice. 

How do AI systems make adaptive, learning-powered decisions in domains from personalised health apps to digital platforms and large language models? This module introduces methods for adaptive, learning-powered decision making, starting with experimentation and bandits, and building toward reinforcement learning and learning from human feedback.

You will explore applications including refugee assistance, recommendation systems, online advertising, content moderation and large-scale AI like LLMs. Emphasising intuition and implementation, the module features guided coding assignments, where you extend provided code while tackling realistic challenges, and concludes with a final exam. By the end of the module, you will be able to design, evaluate and deploy adaptive AI systems in practical contexts.

This elective aims to teach you basic analytics skills and methodologies for large-scale data analysis. It will focus on the practical use of these skills and methodologies to solve real world problems. You will cover a wide spectrum of technologies and algorithms in machine learning.

This module explores the quantitative mechanics of digital platforms, focusing on how data, algorithms and advanced experimentation drive modern marketing strategy. You will bridge data-driven frameworks with industry case studies to evaluate how platform interventions influence customer behaviour and broader marketplace dynamics. Ultimately, the module equips you with the analytical expertise needed to optimise decision-making and drive sustainable growth in the digital economy. 

This is a hands-on, experiential, group-based module on how to rapidly build a startup using AI as an accelerant for finding a repeatable and scalable business model. You will learn that a startup is not just the product, it is the sum of all parts of a business model.

Each week you will get out of the classroom to talk to over 10 customers to test their hypotheses about each part of the business model – customer acquisition, channel, pricing, costs, resources and partners. In addition, you will build Minimal Viable Products (MVPs) to test the model and product/market fit. As part of your class project, you will use multiple AI tools to replace what previously would have taken a large development team.

The goal of this module is to introduce students to the basic tools of causal inference and apply these tools in marketing and healthcare analytics applications. The module will also introduce and use recent developments from the intersection of causal inference and machine learning.

This module will give an overview of big data applications and technologies (including Spark etc). It will further cover all associated topics, such as data integration, data privacy, data analysis etc. At the end of the module you should be able to navigate the space of big data solutions.

By studying this module, you will gain an understanding of the energy industry and the key problems faced by participants in energy markets. You'll be exposed to the challenges arising from changes in energy systems, from increasing renewable energy and decarbonisation. Learn about the types of available data for energy markets and the way that these can be used in energy analytics. You'll also gain an understanding of the metrics and models used in the energy industry and the way that these can be used by decision makers. Discover how to communicate with business users in the energy domain and answer their questions through data analytics.

This elective looks at the problems and data sets that arise in financial industries and the models and business requirements of the finance sector. It includes hands-on practical analysis of financial industry-specific analytic data sets using the methodologies learned earlier in the programme.

This module gives a self-contained discussion on the topic of large language models and generative AI. You will discuss the inner mechanism of training these models and study the state-of-the-art models in detail. We will cover the key modelling concepts such as attention mechanism and self-supervised training, and will also showcase the intuitions for building these modern machine learning models by visiting how the models evolve over time. While studying these models, we will take a managerial perspective and develop statistical thinking when analysing the business-related data. Our discussion will also be complemented by user cases and data sets from IT, supply chain, retail, finance etc. 

The objective of the module is to understand how analytics is used to identify and understand critical issues in private and public healthcare organisations. We will explore some of the questions and methods that private or public organisations active in the health domain address with data. At the end of the module you will have a good understanding of the challenges specific to health and healthcare. To do so, we will consider the problems that arise in the healthcare market, and the models and business requirements for healthcare ecosystems to succeed. The module will provide a mixture of theory and hands-on practical analysis of medical, and healthcare analytic specific data sets using the methods learned earlier in the programme.

Success as managers and analytics professionals depends on leading and motivating teams and creating value for the organisation.

In this module, you will look at analytics from a holistic business perspective. You will consider topics such as leadership and how to think like a leader, how to harness your knowledge to gain competitive advantage, and how to manage teams of analytics professionals.

Supply chains are networks of organisations that supply and transform materials and distribute final products to customers. Supply chain management represents a great challenge as well as tremendous opportunity for most firms. If designed and managed properly, supply chains are a crucial source of competitive advantage for both manufacturing and service enterprises. This module focuses on models and analytic tools, which are useful in the design and operations of logistics and supply chains. Strong emphasis is placed on how specific tools can be used to improve the performance and/or reduce the total cost of a supply chain.

In today’s interconnected society, networks arise in many different contexts and a vast amount of networked data is now generated. Social networks such as Facebook, Instagram and Twitter generate an immense amount of data that is invaluable to marketers and businesses to obtain product feedback and do targeted marketing. This module covers algorithms, analysis and interpretation of network data and relationships. The skillset you will develop is key to an integrated marketing analytics approach.

Analytics permeates every aspect of business decisions, especially in marketing. Indeed, today’s marketing is not just an art; it requires the use of data and quantitative approaches to support strategic decisions in rapidly evolving offline and online environments such as retail. As data availability and a variety of new technologies move the retail sector forward, retailers and manufacturers are increasingly seeking marketing managers and analysts with sufficient technical background to generate actionable insights from data and make effective data-driven decisions.

This module focuses on methods for quantitatively analysing text data, such as newspaper articles, social media posts, political speeches, and company product descriptions. The amount and availability of such data is growing rapidly and extracting valuable information from it is an important challenge. In recent years, numerous machine learning methods have been developed for text. This module will introduce you to these methods, but of equal importance will be to discuss their application to problems in economics and finance.

This learning experience is designed for you to work as consultants on a business challenge in an emerging market context. You will apply innovative thinking and problem solving techniques to achieve real commercial, social or economic impact. You will travel to the country where your client is based and deliver the project over an intensive one-week period.

The module will allow you to deploy your interdisciplinary expertise in business strategy, entrepreneurship, innovation, marketing, finance and other areas, as well as your consulting skills to analyse the issue at hand, collect evidence and generate recommendations. You will also learn about working in an economic and cultural context that may be unfamiliar to you.

The module is practical, aimed at generating an output that can make a real difference to the local business and economy. You are encouraged to organise yourselves within groups and act independently to set goals, gather evidence, make contacts and generate conclusions and recommendations. 

The aim of the Global Immersion is to give you a chance to experience first-hand the social and cultural dynamics of business in another country. The trip will consist of a number of visits to different companies across a range of industry sectors, as well as social and cultural activities. Moreover, you will receive the opportunity to network with local ³Ô¹ÏºÚÁÏ alumni and professionals.

This module brings together the methodological and domain-specific knowledge gathered throughout the programme and applies them to a set of business case studies. By the end of this module, you will be able to:

• evaluate the use and impact of business analytics at a firm;
• assess the players, techniques and future of business analytics;
• develop practical skills in applying data analytics tools;
• summarise and support the value of the findings of analytics for the firm.

This is a substantial group-based consulting project undertaken for a real client. You will develop your managerial and consulting skills by applying your knowledge to a real business situation.

The focus is on managing a consulting project from inception to completion, drawing on your knowledge from other modules and combining it with the competencies and skills developed through the professional development programme.

Organisations that have collaborated with our students include: Agilisys, Burberry, Expedia, Johnson and Johnson, PropertyScape, Trench Capital and 55 UK.

The Individual Research Report (IRR) constitutes a small piece of research to be completed over the summer period and is best suited to students who are interested in pursuing a PhD. Students must find a faculty member to supervise them. Typically, only one or two students per year pursue this option, which can be done in place of the Analytics in Business Capstone module.

The Work Placement/Start-up offers the opportunity to apply the programme material to a challenging business situation in which you are fully immersed. The focus is on developing your skills in one specific business context, drawing on the knowledge and skills achieved across the modules. 

In the first part of the module you will use your data science knowledge to solve problems as well as understand the tool to communicate your insights and solutions in ways that persuade and lead people to make change. 

For the second part of the module, you will undertake a Work Placement or Start-up, to further your independent learning.

Work Placement 

Students who choose to take this module must secure their own work placements and submit details of their work placement to the Programme team. 

Start-up 

Students who choose to take this module must submit details of their start-up to the Programme team, who will evaluate if the start-up is relevant to the programme and its learning outcomes. 

With the Extended Work Placement you will access additional work placement opportunities, including longer summer internships in the UK and worldwide. You will gain deeper work experience credentials and boost your marketability with potential future employers.

Employers in Europe often view internships as a critical step in the hiring process, and by participating in longer internships, you can demonstrate your commitment and capabilities, making you a more attractive candidate for future roles. You will also graduate with additional credits, which is particularly valued by employers in Europe.

Please note that additional fees apply for the Extended Work Placement.