Who is it for?
The MSc in Business Analytics programme will help you generate and capture greater competitiveness in data-driven business.
Our Business Analytics programme is designed to provide a foundation to those who will determine the scope and direction of data analytics research within their organisation, and communicate the research outcomes to the ultimate decision-makers.
Our graduates are trained to participate in the strategic management process, improve the organisation’s financial performance and help design the effective measures of performance of an organisation for which evidence-based data become a strategic asset in the decision-making process.
Therefore, the primary goal is to provide an insight into business data analytics and prepare the students to develop the set of skills and attitudes that will evolve into effective leadership skills.
Typical backgrounds of students are Accounting, Biology, Business Administration/Studies, Computer Science, Economics, Engineering, Environmental Studies, Finance, Hospitality Management, Human Resources, Information Systems/Technology, Management, Marketing, Mathematics and Psychology.
The main purpose of the master's in Business Analytics programme is to develop a comprehensive set of skills and to encourage the positive attributes that are essential to becoming a successful business analyst.
The master's degree is committed not only to imparting specialist skills, but also to developing the so-called "soft skills” which are important in influencing people and organisations.
As well as obtaining effective and persuasive communication skills, you will also learn about ethics-related issues, which are another key ingredient to responsible leadership.
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
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.
All of our MSc courses start with two compulsory induction weeks which include relevant refresher courses, an introduction to the careers services and the annual careers fair.
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.
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.
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.
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.
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.
In term three you will study:
- Applied research project (20 credits)
- Four electives (10 credits each).
Electives offered in 2021
- Applied Machine Learning
- Applied Natural Language Processing
- Data Management Systems
- 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
- FinTech (taught in Italy)
- Investment Strategy (taught in New York, USA)
- Luxury Marketing Strategy (taught in Paris, France)
- Procurement (taught in Mannheim, Germany)
Please note that electives are subject to change and availability.
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., and some examples are: Bank of England, Ekimetrics, Fiat Chrysler Automobile, Government Actuary's Department, Velador Associates and Vodafone UK. Most of the projects are directly supervised by the industry partner representatives together with our academic staff.
To satisfy the requirements of the degree course, students must complete:
- Eight core modules (15 credits each)
- Four elective modules (10 credits each)
- One applied research project (20 credits).
Assessment of modules on the MSc in Business Analytics, in most cases, is by means of coursework and unseen examination. Coursework takes a variety of formats and may consist of individual or group presentations/reports, set exercises or unseen tests.
There is a compulsory one week induction programme just before Term 1 starts, which is a dedicated introduction to the course and to business analytics. You are required to complete a number of induction workshops at the beginning of the course as follows:
- Team building
- Career induction and careers fair
- Professional development skills.
During this period, a variety of activities are offered to students, to support them in their learning and professional development. Bayes Careers offers workshops with a focus on the key skills that employers are looking for, as well as preparing students for the application process. The annual MSc Careers Fair at this time provides the opportunity to meet more than 60 companies who are recruiting across many sectors including consulting, insurance, finance, energy, and other fields.
During the year you will also get the opportunity to attend employer events such as recruitment sessions designed to make you more aware of the job opportunities and career pathways open to you. There will also be industry information sessions to help you build and maintain your commercial awareness, a key skill which employers are looking for in their candidates. Examples of the employers who are set to meet the Business Analytics students this year are Accenture, British Airways, Ekimetrics and EY.
Bayes Careers also provides a range of workshops and online resources and one-to-one appointments to help you gain key employability skills and information to help you with your career planning and throughout the job search process.
Ask a student
Chat to one of our current master's students now about applying for a MSc at Bayes Business School.
Term dates 2022/23
- Induction: 12th September 2022 - 23rd September 2022
- Term one: 26th September 2022 - 9th December 2022
- Term one exams: 9th January 2023 - 20th January 2023
- Term two: 23rd January 2023 - 7th April 2023
- Term two exams: 24th April 2023 - 5th May 2023
- Term three - international electives: 8th May 2023 - 19th May 2023
- Term three: 22nd May 2023 - 7th July 2023
- Term three exams: 10th July 2023 - 21 July 2023
- Resits: 14th August 2023 - 25th August 2023
- Additional resit week - tests only: 28th August 2023 - 1st September 2023.
Course timetables are normally available from July and can be accessed from our timetabling pages. These pages also provide timetables for the current academic year, though this information should be viewed as indicative and details may vary from year to year.
Please note that all academic timetables are subject to change.
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 MSc in Business Analytics. Vali teaches one MBA module, namely `Data Wrangling and Visualisation`.
Module Leaders of the compulsory Business Analytics core modules
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.
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.
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 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.
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).
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.
How to apply
Documents required for decision-making
- Transcript/interim transcript
- Grading system used by your university
- Current module list (if still studying)
- Personal statement - this should be around 500 words in length and answer the following:
- Why have you selected this course? What are your motivating factors?
- What are your areas of interest within the course?
- What contributions do you feel you can make to the course?
- How do you see the course affecting your career plans?
Documents also required (may follow at later date)
- English language test result if applicable
- Two references, one of which MUST be an academic reference
- For a successful application to receive an unconditional status all documents must be verified, so an original or certified copy of the degree transcript must be uploaded to the application form or e-mailed to the relevant Admissions Officer upon request
We cannot comment on individual eligibility before you apply and we can only process your application once it is fully complete, with all requested information received.
If you would like to arrange an individual appointment to discuss the application process and be given a tour of the facilities, please complete this form.
Please note - these are subject to availability.
Terms and conditions
Students applying to study at Bayes Business School are subject to City, University of London's terms and conditions.
- A UK upper second class degree or above, or the equivalent from an overseas institution, in a relevant subject is required.
- Students with a degree that includes quantitative topics are sought and such degrees are: actuarial science, business, computer science, economics, engineering, finance, geography, mathematics, psychology, sociology, statistics or any other quantitative social science.
- Work experience is not a requirement of this course.
- GMAT is not required for application, but may be requested as a condition of offer at the discretion of the Admissions Panel.
English language requirements
If you have been studying in the UK for the last three years it is unlikely that you will have to take the test.
If you have studied a 2+2 degree with just two years in the UK you will be required to provide IELTS results and possibly to resit the tests to meet our requirements.
- The required IELTS level is an average of 7.0 with a minimum of 6.5 in the writing section and no less than 6.0 in any other section.
Fees in each subsequent year of study (where applicable) will be subject to an annual increase of 2%. We will confirm any change to the annual tuition fee to you in writing prior to you commencing each subsequent year of study for continuing students (where applicable).
Deposit: £2,000 (usually paid within 1 month of receiving offer and non-refundable unless conditions of offer are not met)
First installment: Half fees less deposit (payable during on-line registration which should be completed at least 5 days before the start of the induction period)
Second installment: Half fees (paid in January following start of course)
Career destinations for MSc Business Analytics
Our MSc Business Analytics degree is designed to provide you with the fundamental skills required for a successful career as a Business Analyst. Data-driven business is a growing trend, with analytical skills and essential business knowledge prized by employers who are keen to drive their business forward. You will develop key data analytics skills alongside the soft skills required to influence and lead a business forward. The degree explores business processes that are core for any successful organisation, such as management, finance and measuring performance.
Graduates move into a variety of positions helping many organisations enhance their practices and pave the way for success.
Our dedicated Careers Team will help you identify your ideal career path and work with you to maximise the potential of accomplishing your professional goals.
Class of 2020 profile
Recent graduates from 2020 have secured positions for organisations such as:
- Business Analysis Trainee - Directorate-General for Innovation and Technological Support - European Parliament
- Consultant - Technology Risk - EY
- Junior Analyst - Data Department - Photobox
- Data Analyst - Global Data - Bloomberg
- Professional Services Graduate - Global Accounts - Amazon Web Services
The remaining industries are made up of 62%.
Data provided from alumni who completed the annual destination data survey 2019/20