The increase in the volume, variety, and velocity of data creates opportunities for businesses to improve decision making and develop new data driven products and services. The MSc Business Analytics has been developed to meet the demand for qualified professionals, who possess the necessary expertise to realise end-to-end business analytics solutions and are equipped to utilise data for business decision-making purposes.
The programme is built around the three core areas needed to succeed in analytics: business knowledge, statistics, and computing. This includes modules focusing on the application of analytics in core business functions such as marketing and human resources, as well as modules focusing on developing and applying technical skills such as advanced analytics and machine learning, text mining, data management, and data driven decision making.
In total, students will study eight modules in addition to pre-course training and a final dissertation project. The dissertation is a key highlight of the programme, as it provides students with the opportunity to undertake an independent project where they will create a technical business analytics solution incorporating all elements of the programme.
The programme will include an induction, where pre-course training in key statistics and computer skills will ensure students from a range of backgrounds have the necessary skills to undertake the course.
Business Analytics highlights
Student Experience
Students will learn how to use state-of the-art, industry standard software over the duration of the programme. This includes software such as R, Python, KNIME, and Tableau.
In addition, Queen’s University Belfast has benefitted from an in-kind donation from the US-based Alteryx Software, valued at approximately $15 million. This generous gift includes licenses that provide students with free access to software, training, and accreditation in data analytics. This transformative partnership will equip our students with competitive skills from day one.
Industry reports show a global shortage for data scientists. Students will learn to use cutting edge and industry standard tools and techniques to enable career development.
Students on the course can benefit from the QBS Postgraduate Employability Programme. This initiative aims to enhance core employability skills through interactive workshops, informational sessions, and employer presentations. Each session is crafted to boost career confidence, foster active participation in skills development exercises, and deliver tangible outcomes for students.
Industry Links
Developed by staff with industry and academic backgrounds, the course is tailored towards the key skills required to succeed in a business analytics role.
Data Duo Mentoring – Some students on the programme will also get the chance to engage in two way mentoring with businesses, providing advice on how to to utilise their business data, and in turn gaining valuable career and professional mentoring (places dependent on business demand, usually approx. 15 places).
Data for Good hackathon - In partnership with the QBS Analytics Lab, students also have the opportunity to participate in the annual Data for Good hackathon. This involves working with industry mentors to develop data driven solutions aimed at solving some of society's most important problems. See more details in link below: https://www.qub.ac.uk/schools/queens-business-school/business-hub/analytics-lab/
World Class Facilities
Queen’s Business School (QBS) has recently undergone an innovative expansion that establishes a benchmark of global excellence for one of the top business schools in the UK and Ireland. A stunning new 6,000 square metre building, adjacent to the listed red-brick Riddel Hall has been designed with the latest digital infrastructure for media lecture capture, TED Talk provision and collaborative breakout sessions.
Fostering an enhanced social and educational experience the new state-of-the-art QBS venue boasts a 250-seat tiered educational space; 120-seat Harvard style lecture theatre; 150-seat computer laboratory; breakout study spaces; FinTrU Trading Room; a café, and a Business Engagement and Employability Hub.
MSc Business Analytics students can expect to study the following modules:
Semester 1
*Statistics for Business
*Data Management
*HR Analytics
*Artificial Intelligence in Business and Society
Semester 2
*Advanced Analytics and Machine Learning
*Data Mining
*Data Driven Decision Making
*Marketing Analytics
Semester 3
*Dissertation
The dissertation provides students with the opportunity to undertake an independent project. This will involve the development of a technical business analytics solution incorporating elements from the course. The suggested technologies for the solution will be those covered in the course. The solution should typically include a combination of a database, machine learning, and a visualisation component. It is recognised that in some cases projects may focus on specific components (e.g. storage and processing, predictive analytics, or advanced visualisation and interpretation), and this should be agreed in advance. Students will also be provided with suggestions around potential data sources for use in the project.
In addition to the technical solution, students will be required to produce an initial feasibility study, a technical report, and a written report include a review of the literature, methodology for solving the problem, and results, discussion and conclusions.The module requires students to draw from across the course, incorporating knowledge from the three core business analytics domains: statistics, computing, and business.
Queen's Business School With a background in industry and academia, Dr Graham specialises in helping businesses to gain benefits from the effective use of data for decision making and new products and processes. Dr Graham has industry experience in a major healthcare trust, where he specialised in healthcare informatics. He has also worked in data science consultancy for a big 4 firm. Dr Graham has industry expertise in data science across multiple sectors including healthcare, the legal industry, financial services, and retail. His current research focuses on the application of machine learning and other data science approaches to solve business problems.
Learning and Teaching
Teaching Methods
Learning and teaching methods include a mix of computer lab based sessions and lectures.
Tools and techniques learned in the classroom context will be used to address business problems. This will involve a mix of teaching methods to enable students to build the technical and business expertise required for a successful career in analytics. This includes methods such as computer/software practical demonstrations and training, lectures, tutorials, seminars, problem-centred techniques such as national and international case studies, non-book media (videos and podcasts), individual research, oral presentations, group projects, online discussion forums, industry visits and practitioner workshops. Specific details are provided in the programme specification, including details on assessments.
Assessment
Assessments associated with the course are outlined below:
Assessments will focus on both theory and practical application of business analytics, including the use of data to gain business insights, the development of analytics solutions, essays and group work. It is anticipated that students will have approximately 30 hours direct academic contact time (drawing on methods outlined above) per module. In addition to the direct teaching hours per module, each student will normally be expected to spend approximately 120 hours on individual study time plus time for assessment completion, per module.
What our academics say
With the explosion of information and the global shortage in analytics professionals with the skills needed to turn data into business value, the MSc Business Analytics programme is aimed at graduates who aspire to work in this cutting edge industry. The course has been developed to bridge the gap between analytics and business, and includes the latest topics from across core business and analytics areas. Students who successfully complete the programme will graduate with the technical, statistical, and business skills needed to succeed as an analytics professional.
The information below is intended as an example only, featuring module details for the current year of study (2024/25). Modules are reviewed on an annual basis and may be subject to future changes – revised details will be published through Programme Specifications ahead of each academic year.
Knowledge of the theory and application of probability and statistics is an essential component of business analytics. Statistical methods make up part of the set of tools required in business analytics, and form the basis for more advanced topics such as machine learning and artificial intelligence.
In this module, students will focus on descriptive and inferential statistics using the R programming language. This provides the necessary statistical foundation for business analytics as well as introducing R programming.
Topics may include but are not limited to:
• Descriptive statistics
• Correlation
• Probability
• Distributions
• Hypothesis testing and confidence intervals
• Linear regression with two variables
• Multiple regression
• Assessing performance and assumptions
• Logistic regression
• R programming
Learning Outcomes
Upon successful completion of the module students should be able to:
• Critically evaluate the appropriateness of a range of statistical tests in solving a variety of business and research problems
• Effectively implement statistical procedures manually and programmatically
• Interpret the output of statistical tests and explain their practical and theoretical implications
Skills
This course provides opportunities for the students to enhance the following skills:
Application and interpretation of statistics
Data analysis
Communicating with data
R programming (and general good programming practice)
Analytical and problem solving skills
This module focuses on the application of data mining using python. The module will begin with core coding and data mining tasks in python, before focusing in on mining unstructured data. Much of the data produced today is unstructured, such as social media posts, textual documents, images and video. Mining this unstructured data provides businesses with the opportunity to gain substantial benefits through the development of new and improved products and processes and improved decision making. Extracting value from unstructured data requires additional tools and techniques, compared with those required to analyse smaller structured datasets.
This module covers the key analytics tools and techniques needed to gain value from unstructured data. The module will cover the variety of sources and uses of unstructured data, with a particular focus on the practical analysis of textual data. The module will be delivered using the python programming language, which is one of the most popular coding languages in analytics. It therefore also serves to introduce students to this important coding language, complementing the R coding skills developed on other modules.
Course content may include, but is not limited to:
Python Coding
Applications of unstructured data analytics
Sources of unstructured data
Processing, exploring and visualising textual data
Supervised and unsupervised learning with unstructured data
Ethical considerations in the use of unstructured data
Learning Outcomes
Upon successful completion of the module students should be able to:
• Critically evaluate the role of data mining and unstructured data in organisations
• Develop and communicate data mining solutions using python
• Develop and communicate machine learning solutions using unstructured data
Skills
This course provides opportunities for the students to enhance the following skills:
Python coding skills
Exploration and visualisation of unstructured data
Supervised learning with unstructured data
Unsupervised learning with unstructured data
The dissertation provides students with the opportunity to undertake an independent project. This will involve the development of a technical business analytics solution incorporating elements from the course. The suggested technologies for the solution will be those covered in the course. The solution should typically include a combination of an appropriate data storage solution, data exploration and visualisation and machine learning and/or other advanced analytics approaches. It is recognised that in some cases projects may focus on specific components (e.g. storage and processing, predictive analytics, or advanced visualisation and interpretation), and this should be agreed at the proposal stage. Students will also be provided with suggestions around potential data sources for use in the project.
In addition to the technical solution, students will be required to produce an initial feasibility study, a technical report, and a written report including a review of the literature, methodology for solving the problem, results, discussion and conclusions.
The module requires students to draw from across the course, incorporating knowledge from the three core business analytics domains: statistics, computing, and business.
Learning Outcomes
On completion of the dissertation students should be able to:
• Undertake and manage a small business analytics project.
• Develop a business analytics solution.
• Critically evaluate the role of the solution in solving a specific problem, and in particular the strengths and limitations of the solution in solving the problem.
• Evaluate the legal and ethical implications of the solution.
• How to synthesise, analyse, interpret, and critically evaluate information from a variety of different sources
Skills
On successful completion of this dissertation students should have gained the following skills:
• Search and critically review relevant literature
• Creative thinking and problem solving
• Technical product development
• Business analytics project management
• Report writing
• Time management
The dissertation provides students with the opportunity to undertake an independent project. This will involve the development of a technical business analytics solution incorporating elements from the course. The suggested technologies for the solution will be those covered in the course. The solution should typically include a combination of a database, machine learning, and a visualisation component. It is recognised that in some cases projects may focus on specific components (e.g. storage and processing, predictive analytics, or advanced visualisation and interpretation), and this should be agreed in advance by the students supervisor. Students will also be provided with suggestions around potential data sources for use in the project.
In addition to the technical solution, students will be required to produce a written report include a review of the literature, methodology for solving the problem, and results and conclusions.
The module requires students to draw from across the course, incorporating knowledge from the three core business analytics domains: statistics, computing, and business.
Learning Outcomes
On completion of the dissertation students should be able to:
• Undertake and manage a small business analytics project.
• Develop a business analytics solution.
• Critically evaluate the role of the solution in solving a specific problem, and in particular the strengths and limitations of the solution in solving the problem.
• Evaluate the legal and ethical implications of the solution.
• How to synthesise, analyse, interpret, and critically evaluate information from a variety of different sources
Skills
On successful completion of this dissertation students should have gained the following skills:
• Search and critically review relevant literature
• Creative thinking and problem solving
• Technical product development
• Business analytics project management
• Report writing
• Time management
Artificial intelligence (AI) has already had a substantial impact on business and society, such as data driven business strategies, changes to the nature of work, the development of innovations which shape the behaviour of individuals and society, privacy and surveillance concerns, and recent ethical crises in the use of data.
With the fast pace of AI development, these trends seem likely to continue, making it essential to consider the wider implications of AI on business and society. This module will encourage students to engage with these issues, building a deeper understanding of the wider implications of AI, and how students can contribute to responsible development and use of AI in their future career.
Course content may include, but is not limited to:
The strategic implications of AI innovations for business
The wider economic and societal consequences of AI
Changes in the nature of work due to AI
Ethical use of data
Surveillance and privacy considerations in the use of data
Legal consideration in the use of data
Learning Outcomes
On successful completion of this module students should be able to:
• Critically evaluate the implications of AI for society
• Critically evaluate the implications of AI for businesses
• Explain the legal and ethical considerations of AI
Skills
Subject-specific Skills
• Critical evaluation of the wider business, and societal consequences of AI.
Cognitive Skills
• Problem solving
• Logical reasoning
• Independent enquiry
• Critical evaluation and interpretation
• Self-assessment and reflection
Transferable Skills
• Synthesise and evaluate information/data from a variety of sources
• The preparation and communication of ideas in written form
• Work both independently and in groups
• Organisation and time management
• Problem solving and critical analysis
The analysis of data is only useful if it contributes to improvements in business decision making. This module explores how businesses use data for making business decisions. This includes a focus on gaining business insights from the effective management and analysis of data, data visualisation and storytelling, and prescriptive analytics techniques. Students will have the opportunity to work with advanced visualisation and optimisation software such as tableau, excel, and R. The module will also consider the people side of analytics, placing analytical techniques for decision making in a business context, considering the managerial and organisational factors involved in becoming a data driven organisation.
Module content may include but is not limited to:
The role of analytics in decision making, at both operational and strategic levels
Data Visualisation: visualisation of a variety of types of data such as numeric, text, and geospatial data.
Prescriptive analytics and optimisation
The role of data driven decision making in organisations
Benefits, barriers, and limitations of data driven decision making
Ethical considerations in the use of data in decision making
Appreciation of the cultural differences in the use of data, and the potential for data to be used in wider national and international decision making (e.g. sustainable development, disaster planning, corporate social responsibility)
Learning Outcomes
Upon successful completion of the module students should be able to:
• Critically evaluate the use of data and analytics for decision making in organisations
• Design advanced data visualisations to solve complex business problems
• Design solutions to carry out prescriptive analytics tasks such as automated decision making and optimization
• Critically evaluate the legal and ethical considerations in the use of data for decision making.
Skills
This course provides opportunities for the students to enhance the following skills:
Identifying opportunities for data driven decision making in organisations, and the ability to execute such approaches to improve organisational decision making.
The ability to present complex data in a format that is comprehensible to a wide range of technical and non-technical audiences.
Critically reflect on the role of data in business decision making
The ability to use tools to develop advanced and effective data visualisations
The ability to use analytical techniques to develop prescriptive solutions to business problems
The effective use of human resource (HR) data can enhance human resource management (HRM) and thus wider organisational performance. This module will consider the practical use of data in HRM, through applications such as monitoring and evaluating employee activity and performance, predicting future performance and predicting employee attrition. The module will also consider the theoretical basis for the use of data in HRM, thereby linking the practical side of people analytics with HRM theory.
Course content may include, but is not limited to:
Introduction and overview to HR analytics.
The strategic and operational role of HR analytics within an organisation.
Monitoring and enhancing the performance of human resources using data.
The applications of analytics to HRM, and the theoretical basis for these applications.
Descriptive and visual analytics with HR data.
Predictive analytics with HR data.
Ethical considerations with HR analytics.
Learning Outcomes
On successful completion of this module students should be able to:
• Explain the value and limitations of HR analytics from both a theoretical and practical perspective.
• Appraise the antecedents and consequences of integrating HR analytics into human resource functions and the wider organisational implications.
• Use leading software tools to carry out HR analytics, including descriptive, visual, and predictive analytics.
Skills
Subject-specific Skills
• Application of HR analytics to improve human resource functions
• The use of software for HR analytics
Cognitive Skills
• Problem solving
• Logical reasoning
• Independent enquiry
• Critical evaluation and interpretation
• Self-assessment and reflection
Transferable Skills
• Synthesise and evaluate information/data from a variety of sources
• The preparation and communication of ideas in written form
• Work both independently and in groups
• Organisation and time management
• Problem solving and critical analysis
The availability of data and analytics tools has resulted in substantial opportunities for companies to derive benefit from the application of marketing analytics. Marketing analytics has grown to be one of the key areas within business analytics, with most large companies deriving benefit. This module focuses on the application of analytics techniques to marketing problems, highlighting the operational and strategic benefit. In this module, students will learn how analytics can be applied to 4 strands of the marketing mix. The module will explore several methodologies that can be used for achieving analytics driven marketing decisions. The course will provide the necessary fundamentals and will serve as a strong foundation for aspirants aiming to explore the fast-evolving area of modern marketing.
Course content may include, but is not limited to:
• Overall scope and applicability of analytics to marketing decisions
• Applications to analytics (descriptive, predictive, and prescriptive) to selected specific aspects of marketing mix such as:
o Unsupervised learning for Customer segmentation and product design
o Predictive and prescriptive analytics for pricing
o Supervised leaning for customer retention
Learning Outcomes
Upon successful completion of the module students should be able to:
• Critically evaluate the applications, benefits, and limitations of marketing analytics
• Design and develop solutions to marketing problems, drawing on various analytics techniques
• Implement and test the solutions
• Analyse the business implication of analytical approaches in marketing decision-making
Skills
This course provides opportunities for the students to enhance the following skills:
Design of analytics solutions to solve marketing problems
Supervised and Unsupervised learning techniques
Prescriptive analytics models
Machine learning is the core technology underpinning predictive analytics and artificial intelligence, as well as many other analytical tasks.
This module will build on the skills developed in the statistics module in terms of both programming and more advanced statistical techniques, namely the application of machine learning algorithms.
Topics may include but are not limited to:
• The analytics process
• Analytics tools
• Feature selection
• Supervised learning
• Unsupervised learning
• Evaluating model performance
• Programming machine learning models
• Evaluation of the ethical implications of the use of algorithms e.g. the potential for reinforcing bias, security and privacy.
Learning Outcomes
Upon successful completion of the module students should be able to:
• Critically evaluate a range of analytics tools and algorithms
• Understand and apply key programming concepts as they pertain to machine learning
• Design a predictive analytics solution
Skills
This course provides opportunities for the students to enhance the following skills:
Application of advanced algorithms for business decision making
Programming skills
Problem solving
The effective management of small and big data is a crucial component of all business analytics projects.
This module explores the theory and practice of managing data, including identifying and extracting data, data pre processing, data quality, data warehousing, relational databases, and big data solutions.
Course content may include, but is not limited to:
Structured and unstructured data
Data acquisition
Data extraction using SQL
Data storage (relational database management systems)
Big data solutions
Data preparation
Data quality
Security, legislation and ethical considerations
Learning Outcomes
Upon successful completion of the module students should be able to:
• Evaluate the usefulness of a range of data sources and types in business decision making
• Design a data management solution
• Critically evaluate the main security, legal, and ethical considerations in the management of information
Skills
This course provides opportunities for the students to enhance the following skills:
Database design
Data extraction and wrangling
Data storage
Data management, including SQL and other big data technologies
Normally a 2.1 Honours degree or equivalent qualification acceptable to the University in any discipline to include one module in a quantitative area* or quantitative dissertation. Relevant employment experience in a quantitative area may be considered in lieu of a module in a quantitative area and will be considered on a case-by-case basis.
*This could be a course or module in a broad range of quantitative areas such as Finance, Mathematics, Statistics, Economics, Accounting, Engineering or Physics. Equivalent content from across multiple modules will also be considered.
Applicants are advised to apply as early as possible and ideally no later than 15th August 2025 for courses which commence in late September. In the event that any programme receives a high number of applications, the University reserves the right to close the application portal prior to the deadline stated on course finder. Notifications to this effect will appear on the application portal against the programme application page.
Please note: international applicants will be required to pay a deposit to secure a place on this course.
International Students
Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region. Use the dropdown list below for specific information for your country/region.
English Language Requirements
Evidence of an IELTS* score of 6.5, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required.
*Taken within the last 2 years.
International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
Academic English: an intensive English language and study skills course for successful university study at degree level
Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.
The programme will appeal to students who intend to pursue a career in a business analytics related field, such as data science, business intelligence, consultancy, informatics, or decision intelligence.
Graduates from this programme have secured roles with employers such as KPMG, PwC, EY, Tata Consulting, Citi, FD Technologies, Alibaba Group, Severn Trent. Typical roles include data and business analyst, software engineer, consultant, and senior administrators. https://www.qub.ac.uk/directorates/sgc/careers/
Graduate Plus/Future Ready Award for extra-curricular skills
In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Graduate Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.
1EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.
2 EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.
3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.
All tuition fees quoted relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.
Terms and Conditions for Postgraduate applications:
1.1 Due to high demand, there is a deadline for applications.
1.2 International applicants will be required to pay a deposit to secure their place on the course. The current mandatory tuition fee deposit payment is £1000 International (Non- EU & EU except ROI).
1.3 This condition of offer is in addition to any academic or English language requirements.
Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.
Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.
Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.
If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.
Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.
There are also additional charges for graduation ceremonies, examination resits and library fines.
How do I fund my study?
The Department for the Economy will provide a tuition fee loan of up to £6,500 per NI / EU student for postgraduate study. Tuition fee loan information.
A postgraduate loans system in the UK offers government-backed student loans of up to £11,836 for taught and research Masters courses in all subject areas (excluding Initial Teacher Education/PGCE, where undergraduate student finance is available). Criteria, eligibility, repayment and application information are available on the UK government website.
