Study options
- Starting in
- September 2025
- Location
- Mile End
- Fees
- Home: £21,500
Overseas: £33,500
EU/EEA/Swiss students
What you'll study
This programme is jointly run by the School of Business and Management and School of Mathematical Sciences. Students will have the opportunity to develop their mathematical and statistical knowledge, as well as gain practical experience of using leading software tools.
The course material aims to equip students with the skills to manage IT and analytics specialists, and to understand the leadership and organisational challenges associated with the digitisation of business processes. It examines how robust findings can be obtained from analytics and applied to the direction of businesses.
The programme is taught using real-world cases from different markets and countries, and draws on the experience of an advisory group of current managers from real digital businesses. Summer placement opportunities with these industry partners may be available.
Structure
- Six compulsory modules.
- Three optional modules.
- Compulsory group research project.
Compulsory/Core modules
This module is the capstone module for the MSc in Business Analytics. Students will work in groups and will be required 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 the group through the process of structuring the analytical problem, obtaining and organising the data, data analysis and presentation of results. Final assessment will be based on individual essays which cover specific aspects of the case and student's reflection in the light of Business Analytics methods and theories.
The Masterclass in Business Analytics introduces students to current industrial and commercial business analytics practices. This is done through three components: 1. A hands-on experience with industry-popular Machine-Learning software packages; 2. Descriptions of recent Big-Data projects, initiatives and business models from leading corporations and organisations; and 3. Direct interaction with London-based industry experts through class presentations.
The module will familiarise students with the fundamentals of effective leadership in analytical initiatives/project teams including the difference between leading and managing initiatives/projects, dealing with resistance, knowledge hoarding and different stakeholder interests, transactional versus transformational leadership, inspiring peers and subordinates effective communication, trust and knowledge sharing within and across teams and other stakeholders, presenting and pitching concepts and results, managing the organisational synergy of a team and dealing with acceleration and over-acceleration in analytical projects.
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. The module will then provide an overview of the emerging literature on the application and use of big data and data analytics within organisations.
This compulsory module is taught in Semester 2 building on statistical methods in Data Analytics module in Semester 1. The module introduces students to the problem of causal inference, theories of causality and causal effects empirical methods. The focus is on randomised controlled trials in similar settings. Students learn about different econometric techniques to identify causal effects and their strengths and weaknesses. Data collection and organisation of real or natural experiments, data analysis and reporting results to non-specialists is covered.
This 0-credit module covers Mathematics and Statistics topics which are useful for the different quantitative modules and MSc dissertations. The Mathematics topics include: linear and non linear equations, differentiation, growth and discounting and logarithms. The Statistics topics include: descriptive statistics, probabilities and distributions.
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 module introduces statistical modelling, regression analysis, and machine learning, and the use of the R software environment in analyzing data.
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.
Elective modules
This module is key 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 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.
This module will present methods for time series analysis.These will allow the student to understand better how to use and extract information from historical business data series. In particular, the student will learn how to extract the pivotal concepts of time series data, including the trend and cyclic components of a data series, calculate the autocorrelation, learn about autoregressive and moving average models, and cointegration.The module will develop the notions around realistic business examples and an implementation of the methods will be provided using the statistic software R..
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.
This module will provide students with a general understanding of current applications of data analytics to finance and in particular to derivatives and investment banking. It will introduce a range of analytical tools such as volatility surface management, yield curve evolution and FX volatility/correlation management. It will also provide you with an overview of some standard tools in the field such as Python, R, Excel/VBA and the Power BI Excel functionality. Students are not expected to have any familiarity with coding or any of the topics above, as the module will develop these from scratch. It will provide you with the understanding of a field necessary to prepare for a career in finance in roles such as trading, structuring, management, risk management and quantitative positions in investment banks and hedge funds.
The structure and dynamics of various complex networks (e.g. World Wide Web, online social, intra/interorganisational, im/export trade networks) are examined. A unified theoretical framework to analyse sociologically relevant phenomena exhibiting complex dynamic network structures (e.g. information diffusion, cultural fads, financial crises, and viral marketing) is the aim. Innovation, to uncover the structural foundations of knowledge creation, transfer, sharing, and diffusion in various empirical domains is emphasised from an interdisciplinary perspective by combining current research on complex networks with contributions from relevant organisational and sociological research.
The module will focus on project management techniques, methodologies, theories appropriate to projects that deliver complex outcomes in a context of high uncertainty on the desired result. The module will also provide team and teaming management principles and practices needed to obtain the desired project management results within time, budget and quality. Students will be encouraged to take advantage of opportunities to earn an accreditation for project management and the course will prepare students for this additional examination.
This module integrates the theory and practice of innovation and entrepreneurship. We will also help develop your skills, abilities and behaviour towards entrepreneurial venturing, whether in established organisations or new ventures. Although business is an important context for this course, the process, skills and ideas we will address are also important for social, sustainability and third sector innovation, and intrapreneurial activities inside established organisations. We will also address broad issues about entrepreneurship, and how it can lead to social benefits and economic value. The module is intended to draw together learning from many different functional areas that students will have already covered in the past - marketing, strategy, finance, law etc. - and place these within the larger context of innovation and entrepreneurship. While we will discuss many tools, models, and frameworks that can assist innovation and entrepreneurship processes, a core focus within the course is to critically analyse and apply these ideas.
Please note that all modules are subject to change.
Assessment
Summative assessment of taught course units will use various methods, ranging from conventional academic coursework and examinations through to shorter specific exercises and analyses of data. Formative assessment will take place using class presentations and debates, short written exercises and group work.
Work completed for the Masterclass and Group Project modules are examined on the basis of a written report, a formal oral presentation, and a demonstration of the insights gained from the projects. The projects will have two examiners each, with a third if there is disagreement.
Research project
You will work in a group to provide analysis of a problem or question, using complex data from a business context. This data is usually supplied by a partner company.
—"The world-class facilities and educators have helped me to better understand coding and implement it in real-world situations. For our final project, we had the chance to experience working with one of the top fintech companies in London. Queen Mary has so many perks to offer, other than just being a student!"
Sarita Saraswati, Business Analytics MSc, 2022
Teaching
Our interdisciplinary approach ensures a fully rounded business education, and includes lectures led by experienced academics and interaction with Queen Mary’s independent Virtual Learning Environment, QMplus.
You will gain invaluable insight from guest speakers, get hands-on experience using industry-leading software and take part in interactive seminars, presentations and lively group discussions with your peers.
You’ll be assigned an Academic Advisor who will guide and support you in both academic and pastoral matters, throughout your course.
Our lecturers also publicise their office hours, when they are available to give feedback and advice on coursework, on their online staff profiles.
Where you'll learn
Facilities
- ThinkPod interactive collaboration space with presentation, recording and video conferencing facilities.
- Free subscriptions to publications such as Bloomberg and Financial Times.
- Additional self-study content available through DataCamp.
- Access to a wide range of software packages, including SAS and SPSS.
- 24-hour library on campus.
Campus
Teaching is based at Queen Mary’s Mile End campus, one of the largest self-contained residential campuses in the capital. Our location in the heart of London’s East End offers a rich cultural environment.
We have invested £105m in new facilities over the past five years to offer our students an exceptional learning environment. Recent developments include the £39m Graduate Centre, providing 7,700 square metres of learning and teaching space.
The campus is 15 minutes from Central London by tube, where you will have access to many of the University of London’s facilities, including the Senate House library.
About the School
School of Business and Management
We invite our students to ask incisive questions, to challenge their assumptions, and to search for solutions to real-world challenges.
The School is accredited by the Association to Advance Collegiate Schools of Business (AACSB), which ensures that the highest standards of excellence in teaching, research, curriculum, and learner success are met.
In the most recent Research Excellence Framework (REF 2021), the School of Business and Management dramatically moved up the Times Higher Education rankings. Among 108 UK business schools, the School now ranks:
- 22nd for overall research quality (up from 39th in REF2014)
- 28th for research outputs (up from 34th)
- 12th for research impact (up from 24th)
- 21st for research environment (up from 59th)
School of Mathematical Sciences
Research in the School of Mathematical Sciences covers a range of subjects in pure and applied mathematics, and is consolidated into research groups reflecting the School's key strengths.
We've invested approximately £18 million in our building to provide state-of-the-art facilities for staff and students. We hold an Athena Swan Bronze award.
Career paths
The university's close connections with businesses in the United Kingdom and France provide students with opportunities to network with practitioners during guest lectures and project work done for the businesses as clients.
This course provides graduates with an excellent foundation to pursue roles in data analysis and consulting, in a variety of different industries.
Alumni from the course are now working in roles such as: Data Science Specialist, Data Analyst, Digital Strategy Analyst, Product Specialist and Senior Business Intelligence Developer.
—"According to recent industry research, the biggest skills gaps in the technology sector are found in the field of business analytics; therefore, graduates who have good intuition and can translate statistical data intelligently are ‘rock stars’ in the organisations they work for."
Dr Georg Von Graevenitz, Programme Director, Business Analytics MSc
Fees and funding
Full-time study
September 2025 | 1 year
- Home: £21,500
- Overseas: £33,500
EU/EEA/Swiss students
Conditional deposit
Home: Not applicable
Overseas: £2000
Information about deposits
Queen Mary alumni can get a £1000, 10% or 20% discount on their fees depending on the programme of study. Find out more about the Alumni Loyalty Award
Funding
There are a number of ways you can fund your postgraduate degree.
- Scholarships and bursaries
- Postgraduate loans (UK students)
- Country-specific scholarships for international students
Our Advice and Counselling service offers specialist support on financial issues, which you can access as soon as you apply for a place at Queen Mary. Before you apply, you can access our funding guides and advice on managing your money:
Entry requirements
UK
Degree requirements
A 2:1 or above at undergraduate level in any subject, provided the degree contains good levels of study of Mathematics and Statistics. Subjects likely to contain sufficient quantitative elements include Mathematics, Sciences, Engineering, Computer Science, Economics and Finance.
Students from less quantitatively oriented degrees, such as Accounting, Management and Politics, are welcome if they have focused on the more quantitative elements of those degrees.
Other routes
Candidates that do not currently meet the set entry requirements may also have the option to study the Graduate Diploma in Finance and Economics. Meeting the required grades on completion of this programme will provide a pathway to study MSc Business Analytics.
Find out more about how to apply for our postgraduate taught courses.
International
English language requirements
The English language requirements for our programmes are indicated by English bands, and therefore the specific test and score acceptable is based on the band assigned to the academic department within which your chosen course of study is administered. Note that for some academic departments there are programmes with non-standard English language requirements.
The English Language requirements for entry to postgraduate taught in the School of Business and Management falls within the following English band:
Band 4: IELTS (Academic) minimum score 6.5 overall with 6.0 in each of Writing, Listening, Reading and Speaking
We accept a range of English tests and qualifications categorised in our English bands for you to demonstrate your level of English Language proficiency. See all accepted English tests that we deem equivalent to these IELTS scores.
Visas and immigration
Find out how to apply for a student visa.