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Business Analytics (MSc)

MSc|Postgraduate Taught

Business Analytics

Entry year
Entry requirements
1 year (Full Time)
Places available
Open (Full Time)

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. 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, 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 project will involve the application of the business, technical, and statistical skills learnt during the taught modules.

The programme will include an induction course, 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

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.

Career Development

  • 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.

World Class Facilities

  • The MSc Business Analytics is taught in the landscaped setting of Riddel Hall which features excellent facilities, including a dedicated computer lab with the latest analytics software.

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.
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.

Dr Byron Graham, Programme Director

Course Structure

MSc Business Analytics students can expect to study the following modules:

Semester 1*Statistics for Business
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
Hypothesis testing and confidence intervals
Linear regression with two variables
Multiple regression
Assessing performance and assumptions
Logistic regression
R programming

*Data Management
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

*HR Analytics
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.

*Artificial Intelligence in Business and Society
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
Semester 2*Advanced Analytics and Machine Learning
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.

*Data Mining
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

*Data Driven Decision Making
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)

*Marketing Analytics
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
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 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.

People teaching you

Programme Director

Queen's Management 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.

Career Prospects

The MSc Business Analytics 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

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.


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.




The information below is intended as an example only, featuring module details for the current year of study (2022/23). 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.

  • Year 1

    Core Modules

    Marketing Analytics (15 credits)
    Data Mining (15 credits)
    Data Management (15 credits)

Entrance requirements

Normally a 2.1 Honours degree or equivalent qualification acceptable to the University in any discipline to include one module in a quantitative area*. 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 an area such as Finance, Mathematics, Statistics, Economics, or Physics.

Applicants are advised to apply as early as possible and ideally no later than 11th August 2023 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. Notifications to this effect will appear on the Direct 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.

For more information on English Language requirements for EEA and non-EEA nationals see:

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.

INTO - English Language Course(QSIS ELEMENT IS EMPTY)

Career Prospects

The MSc Business Analytics 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

Additional Awards Gained(QSIS ELEMENT IS EMPTY)

Prizes and Awards(QSIS ELEMENT IS EMPTY)

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.

Tuition Fees

Northern Ireland (NI) 1 £8,360
Republic of Ireland (ROI) 2 £8,360
England, Scotland or Wales (GB) 1 £8,360
EU Other 3 £23,100
International £23,100

MSc (T) Finance 

1 EU 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 are for the academic year 2023-24, and relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.

More information on postgraduate tuition fees.

Additional course costs

All Students

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.

Business Analytics costs

There are no specific additional course costs associated with this programme.

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. Criteria, eligibility, repayment and application information are available on the UK government website.

More information on funding options and financial assistance.

International Scholarships

Information on scholarships for international students, is available at

How to Apply

Apply using our online Postgraduate Applications Portal and follow the step-by-step instructions on how to apply.

Apply now

When to Apply

The deadline for applications is normally 30th June 2021. In the event that any programme receives a high volume of applications, the university reserves the right to close the application portal earlier than 30th June deadline. Notifications to this effect will appear on the Direct Entry Portal (DAP) against the programme application page.

Terms and Conditions

The terms and conditions that apply when you accept an offer of a place at the University on a taught programme of study.
Queen's University Belfast Terms and Conditions.

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