| Program start date | Application deadline |
| 2023-09-01 | - |
Program Overview
Who is it for?
Choose the MSc Business Analytics programme to support your career progression if your aim is to generate and capture greater competitiveness in data-driven business. You’ll appreciate our technology-aided learning-by-doing teaching style, academic rigour and authentic experiential learning.
You don’t need previous experience of analytics skills, or the technology enablers needed to deploy them. Instead, you can join our pre-courses which equip you with the minimum knowledge, such as Python and R Programming, before you start your one year MSc Business Analytics Programme.
You’re likely to already have at least an upper second class degree, or the equivalent, in a subject that includes quantitative topics, such as accounting, biology, business administration/studies, computer science, economics, engineering, environmental studies, finance, hospitality management, human resources, information systems/technology, management, marketing, mathematics or psychology.
Studying the MSc Business Analytics enables you to deal with business problems by using different tools and methods on raw data, such as data visualisation and machine learning. I secured a role as a Technology Risk Consultant at EY.
I audit client IT systems, interview clients and review their data in order to analyse their technology risk. What Bayes taught me is more than academic; I learned how to transition from a student to a young professional.
- Mengjia Hang
Objectives
On our Master’s in Business Analytics programme you’ll develop a comprehensive set of skills and nurture the positive attributes essential to becoming a successful business analyst. You’ll learn the specialist, technical, skills of data analytics, and soft skills, such as effective persuasion, communication and business ethics, important for influencing people and leading organisations.
You’ll benefit from the distinctive experiential learning element of your business analytics MSc. Each year we work with industry partners creating tailored dissertation projects to suit your skills and interest and provide you with hands-on real-life work experience. You can choose from our available projects or find your own choice of industry partner. Your industry partner provides data and domain knowledge and plays an advisory role, while Bayes Business School experts give academic support.
Recent students have chosen from twenty projects offered by analytics consulting firms, finance and insurance companies and well-known retailers, including Bank of England, Ekimetrics, Government Actuary's Department, Rolls-Royce and Vodafone UK
We really appreciate being able to work with students from the Business School. They get involved in a variety of exciting projects (such as analysing workforce and mortality data) and provide us with fresh insights from this information.
Government Actuary’s Department, Partner in the Industry Partnership Programme
Working with students from Bayes Business School is great. It allows us to work on new projects and they bring a different skillset to the table with a fresh perspective that helps us look at problems through new lenses.
Vodafone UK, Partner in the Industry Partnership Programme
Program Outline
Structure
- Work directly with industry partners on your dissertation project, learn in the real world and build professional connections.
- Build skills and connections that equip you for a career in the fast growing area of data-driven business.
- Explore business processes that are core for all successful organisations, including management, finance and measuring performance.
- Learn from teaching staff who are applying data analytics to live issues and consulting to the private and public sectors.
On the master's in Business Analytics, you will learn to:
- Extract valuable information from the data in order to create a competitive advantage
- Make use of analytical skills to evaluate and solve complex problems within the organisation’s strategic perspective
- Present and explain data via effective and persuasive communication
- Show commercial focus and the ability of strategic thinking
- Demonstrate depth and breadth of using analytical skills to interrogate data sets
- Illustrate professional integrity and show sensitivity towards ethical considerations.
Induction Weeks
All of our MSc courses start with two induction weeks which include relevant refresher courses, an introduction to the careers services and the annual careers fair.
Pre-study modules
The MSc in Business Analytics starts online in the summer before the beginning of term 1 with three pre-courses which ensure that every student has the minimum specific background required by all other modules.
These subjects are key elements of your course and you are strongly encouraged to complete the modules before you arrive at Bayes in order to avoid being at a disadvantage.
Python and R tutorials run in small groups during the induction week and the first two weeks of Term 1. These tutorials assume that the students are very familiar with the online material from the Introduction to Python and Introduction to R Programming pre-study modules.
Introduction to Python
This module is designed to provide a fundamental understanding of Python programming and no previous programming experience is expected. The teaching model is learning by doing and basic concepts are built up in an incremental manner.
The online material is formulated via multiple Python code examples that enable the students to work independently when dealing with small Python programming tasks.
Introduction to R Programming
This module is designed to provide a fundamental understanding of R programming and no previous programming experience is expected. The teaching model is learning by doing and basic concepts are built up in an incremental manner.
The online material is formulated via multiple R code examples that enable the students to work independently when dealing with small R programming tasks.
This module is designed to prepare you for understanding and performing the computer based exercises and tasks that you encounter in all core MSc in Business Analytics modules and will therefore be completed prior to beginning your course.
Professional Ethics and Good Academic Practice
This module aims to cultivate your awareness of some key ethical issues prevalent in data analysis and statistics, in particular those issues emerging in the applications of modern data science.
You will also develop your awareness of what constitutes good academic practice and learn how to properly reference your work and avoid issues such as plagiarism and poor scholarship in your work.
Term 1
Core modules:
Network Analytics
This module provides on overview of various frameworks and algorithms used in practice to describe and analyse network data―namely information about relations among decision makers (e.g. customers), objects (e.g. products), or decision makers and objects (e.g. customer-product ties).
You should expect to grasp the logic behind modern network science from a practical standpoint. Standard computing skills in Python are required to put in practice the theory discussed during the lectures.
Data Visualisation
This module provides design principles along with frameworks and techniques to synthesise and illustrate complex information via data visualisation This enables you to understand the significance of data by placing such data in a visual context.
You should expect to learn different approaches to data visualisation (e.g., pattern recognition or 'data storytelling') and to be able to adjust these approaches in order to reach different types of audiences.
Analytics Methods for Business
This module provides a collection of standard analytical methods and explains how data analysis is performed in the real world.
They represent an introduction to specific tasks that a business analyst has on a daily basis that ultimately would help in analysing, communicating and validating recommendations to change the business and policies of an organisation.
Furthermore, the module provides the foundation for using the R programming language to translate theory into practice.
Digital Technologies and Value Creation
The Digital Technologies and Value Creation module follows a use case approach and aims to explain how digital technologies could enhance the business opportunities for a firm.
Various real-life applications are provided from problem identification to practical implementations, and the chosen sectors are Marketing Technology (MarTech), People Analytics, Social Media Analytics etc.
This module is not necessarily aimed to develop the core analytics tools, and therefore, the main takeaways of this module is to familiarise the students with contemporary Business Analytics applications that are essential to understand prior to taking the compulsory summer Applied Research Project, which is the knowledge integration part of your education journey during your studies.
Term 2
Core modules:
Applied Deep Learning
The Applied Deep Learning module provides practical implementations of Deep Learning tools into the real world by showing multiple use cases from various sectors, e.g. Recommender Systems and their applications in E-commerce (product recommenders) and social media platforms (content recommenders), Fraud Detection, Digital Marketing etc.
This module is not necessarily aimed to develop a strong Deep Learning foundation, and instead, a learning-by-doing is the main delivery method of the main concepts.
The key takeaway of this module is to familiarise the students with contemporary Deep Learning applications that are essential to understand prior to taking the compulsory summer Applied Research Project, which is the knowledge integration part of your education journey during your studies.
Machine Learning
This module provides an overview of various machine learning concepts, techniques and algorithms which are used in practice to describe and analyse complex data, and to design predictive analytics methods.
You should expect to grasp the main idea and intuition behind modern machine learning tools from a practical perspective. Standard computing skills in R and Python are required to put in practice the theory discussed during the lectures.
Revenue Management and Pricing
The Revenue Management and Pricing module explains how firms should manage their pricing and product availability policies across different selling channels in order to optimise their performance and profitability.
The module aims to explain quantitative models needed to tackle a number of important business problems including capacity allocation, markdown management, e-commerce dynamic pricing, customised pricing and demand forecasts under market uncertainty.
Strategic Business Analytics
This module teaches you how to design, validate and communicate business strategies by using quantitative techniques encountered in all other core MSc in Business Analytics modules.
A strategic consulting approach through real-life case studies is the key ingredient of the module that enables the module leader and invited speakers to illustrate the scope of modern business analytics by providing expert solutions to various chosen real-world problems.
You are trained to develop complex analytical problem-solving skills and hone the critical thinking of a future business analyst.
Term 3
In term three you will study:
Applied research project
The aim of this module is to enable you to demonstrate how to integrate your learning in core and elective modules and then apply this to the formulation and completion of an applied research project. You will be required to demonstrate the skills and knowledge that you have acquired throughout your MSc study.
You will undertake a short piece of applied research on a question of academic and/or practical relevance. Guidelines will be provided in order to help you identify the research question. Based on your chosen topic, you must write a report of around 3,000–5,000 words that summarises and critically evaluates your method and your findings.
In the past, all students were offered projects designed by our industry partners that aim to develop the consulting skills of each student. For the last academic year, students chose from twenty projects offered by various analytics consulting companies, companies from finance and insurance sectors, well known retailers, etc., including: Bank of England, Ekimetrics, Government Actuary's Department, Rolls-Royce and Vodafone UK.
- Four electives (10 credits each).
Electives offered in 2022
- Applied Machine Learning (primarily for Business Analytics)
- Applied Natural Language Processing (primarily for Business Analytics)
- Data Management Systems (primarily for Business Analytics)
- Ethics, Society and the Finance Sector
- Country and Geopolitical Risk Management
- Driving Supply Chain Innovation through Technology
- FinTech - Financial Services in the Digital Age
- New Market Creation
- Retail Supply Chain Management
- Storytelling for Business
International electives
- FinTech (taught in Italy)
- Investment Strategy (taught in New York, USA)
- Luxury Marketing Strategy (taught in Paris, France)
- Procurement (taught in Mannheim, Germany)
Electives are stubject to availability
Career destinations for MSc Business Analytics
With your business analytics master’s you’re ready to help many types of organisation enhance their practices and head for success.
Our graduates work in a variety of roles, but mainly as business analysts or data analysts. Some work for consultancies and professional services businesses, and others for financial firms, banks and technology companies.
Our careers team support you right from the start, with careers induction and a careers fair before you start your studies, workshops to prepare you for the application and interview process and employer events, as well as advice on choosing your career path.
Class profile
Recent graduates from have secured positions for organisations such as:
- Business Analysis Trainee - Directorate-General for Innovation and Technological Support - European Parliament
- Data Analyst - Funds - Bloomberg
- Analyst - Media Monks
- Forensic Data Analytics - Forensic and Integrity Services - EY
- Technology Graduate - Digital Business - HSBC UK
- Technology Consulting Analyst - Analytics - Accenture
Previous graduates have gone on to work in financial services, consulting and data analytics across the UK, Europe and Asia.
(Data provided from alumni who completed the annual destination data survey for 2020/21 and 2019/20).
Dr Vali Asimit - Course Director / Professor in Actuarial Analytics
Vali's research interests include robust supervised (machine) learning, algorithmic bias and prediction fairness in supervised (machine) learning, insurance risk optimisation, and statistical models for extreme/rare events. He used to teach the People Analytics content from the Masters (MSc) in Business Analytics. Vali teaches one MBA module, namely `Data Wrangling and Visualisation`.
Module Leaders of the compulsory Business Analytics core modules
Alan Chalk
Alan is a Data Scientist at Sabre Insurance Company Limited where he applies Machine Learning techniques to insurance related tasks. He started his career as an Actuary and worked in in non-life insurance where his focus was on predictive analytics and pricing. Whilst serving as Global Aerospace Actuary at American International Group (AIG) UK, Alan worked with AIGs dedicated Machine Learning Team. Following this, he expanded his training in statistics and data science with an MSc in Statistics at Sheffield University and an MSc in Machine Learning at University College London. Alan teaches the Term 3 Applied Machine Learning Module. He is also part of the MSc in Business Analytics Industry Partners Programme that offers industry-based projects designed by Alan that aim to enhance the experiential learning when the Term 3 Applied Research Project is developed. Such projects are directly supervised by the industry partner contact person(s).
Dr. Philippe Blaettchen
Philippe is a Lecturer in Management Analytics at Bayes Business School. He is the Module Leader of the Digital Technologies and Value Creation module and the Applied Deep Learning module. Before joining Bayes, Philippe obtained his Ph.D. in Technology and Operations Management at INSEAD, where he taught, among others, Data Science and People Analytics. In his research, Philippe combines machine learning and optimization tools to redesign product and service supply chains in diverse industries ranging from agriculture to healthcare.
Dr Oben Ceryan
Oben is a Lecturer in Operations and Supply Chain Management and he is the Module Leader of the Revenue Management and Pricing module. He obtained his PhD degree in engineering from the University of Michigan. His research interests are in dynamic pricing and revenue management, an emerging area that aims to enhance firms’ profitability by aligning demand with constrained supply through the integration of operations and marketing decisions and that is applicable to a wide range of industries from manufacturing to hospitality, and from online marketplaces to retailing.
Dr Rosalba Radice
Rosalba is a Reader in Statistics and she is the Module Leader of the Analytics Methods for Business module. She obtained her PhD degree in Statistics from the University of Bath. Her research interests are in distributional regression, simultaneous joint equation models, copula regression modelling, generalized additive modelling and applications in wide range of applied areas. She has extensive experience with teaching applied statistics courses including regression models and computational data mining methods. Rosalba co-developed the GJRM package (former SemiParBIVProbit and SemiParSampleSel packages) in R since 2011 that led to over 60,000 downloads; the package is mainly addressed to a wide variety of practitioners that aims to model additive distributional joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.
Hugo de Sousa
Hugo is the module leader at the Strategic Business Analytics module. He is a government and corporate innovation strategist and entrepreneur, with 20 years of experience in Consulting (Head of Innovation), Start-ups (Co-Founder) and Government (CIO/CTO). He also regularly presents at reputable conferences (e.g. TedX) and venues. His career deliverables include an impressive and diverse list of FTSE and high profile companies, consultancies and Public Service entities such as Capita Plc, Lionbridge, Arthur D. Little, Gfi (Inetum) and The Portuguese Govt departments of Justice and Tourism. Hugo holds Academic qualifications in Business Innovation and Entrepreneurship, Computer Science and Management. More recently, Hugo co-founded MettaNoon (Sensory AI and Data Science start-up) and runs a trailblazing innovation community - The Innovation Cafe.
Dr Simone Santoni
Simone Santoni is a Lecturer in Strategy at Bayes Business School, where he leads, among others, the Network Analytics and Data Visualisation teaching modules. He obtained a PhD in Organizations and Markets from the University of Bologna and refined his studies at Columbia University and New York University. Simone's research concentrates on the network foundations of organizations and markets―especially markets for culture and labour. Throughout the years, he has consulted for prominent organizations operating in the recording music industry, theatre, and contemporary art.
Dr Elizabeth Stephens
Dr. Elizabeth Stephens is the Founder Managing Director of Geopolitical Risk Advisory, a consultancy that uses data analytics to advise clients on how geopolitical risks will impact upon their specific trading relationships and investments. For nine years prior to this, she was the Head of Credit Political Risk Advisory at JLT Specialty where she provided corporate clients with strategic advice on the identification, management and mitigation of country risk. Elizabeth has a Ph.D. in International Relations from the London School of Economics and is a guest Lecturer at Bayes Business School and Henley Business Schools, where she delivers Masters and Executive Education courses on Political Risk Management and Business Analytics. Elizabeth is also involved in teaching the Strategic Business Analytics module. She is also part of the MSc in Business Analytics Industry Partners Programme that offers industry-based projects designed by Elizabeth that aim to enhance the experiential learning when the Term 3 Applied Research Project is developed. Such projects are directly supervised by the industry partner contact person(s).
Dr Rui Zhu
Rui is a Lecturer in Statistics and she is the Module Leader of the Machine Learning module. She obtained her PhD degree in statistics from University College of London and her research is in statistical learning, pattern recognition, high-dimensional data analysis and interdisciplinary applications for real-world problems. Rui’s research interests include classification and dimension reduction for high-dimensional data, distance metric learning and real-world applications, such as spectral data analysis, image quality assessment and hyperspectral image analysis.