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
- Developed by staff with industry and academic backgrounds, the course is tailored towards the key skills required to succeed in a business analytics role.
- 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 Riddell Hall which features excellent facilities, including a dedicated computer lab with the latest analytics software.
- 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, SAS, 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
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
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 extraction using SQL
Data storage (relational database management systems)
Big data solutions
Security, legislation and ethical considerations
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.
This course develops the major themes and strategies of Operations Management within both manufacturing and service organisations. The primary objective is to familiarise students with the basic concepts, techniques, methods and applications of operations management. Topics include operations strategy, process design and analysis, capacity management, quality management, lean management, inventory management and supply chain management.
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 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)
*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
This module focuses on a new and exciting development in marketing theory and practice. The use of data, ‘big data’, to assist in marketing decision-making and accountability continues to grow in importance, particularly in the current age of austerity and resource scarcity. The module takes both a theoretical and practical approach to the use of marketing analytics in practice.
A highlight of the module is the use of SAS or SPSS software to analyse data for marketing-related decision-making and evaluative purposes. Students who successfully complete and pass the module will be able to signal to potential employers that they have the theoretical, practical plus industry-standard software skills to compete.
Indicative contents include:
• Introduction and overview of marketing analytics
• Competing on marketing analytics – developing a marketing analytics culture
• Marketing analytics at the strategic, functional, analytical and warehouse levels
• Customer engagement and customer analytics
• Performance implications of marketing analytics
• Current issues and trends in marketing analytics
• The dark side of marketing analytics
Other content focuses on data mining techniques for marketing (including sales and customer relationship management). Taught through instructor led computer workshops using SAS or SPSS software to solve marketing-related problems. Contents include:
• SAS or SPSS training – introduction and overview
• The marketing analytics process
• Data for marketing analytics
• Understanding the customer
• Predicting customer behaviour
• Amalgamating into marketing operations
• Case studies
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
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.
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
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.
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.
For information on international qualification equivalents, please check the specific information for your country.
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: www.qub.ac.uk/EnglishLanguageReqs.
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.
As a result of the COVID-19 pandemic, we will be offering Academic English and Pre-sessional courses online only from June to September 2020.
- 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)
Fees and Funding
Northern Ireland (NI) £6,900 England, Scotland or Wales (GB) £6,900 Other (non-UK) EU £6,900 International £19,900
All tuition fees quoted are for the academic year 2020-21. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.
Additional course costs
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 £5,500 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 £10,609 for taught and research Masters courses in all subject areas. Criteria, eligibility, repayment and application information are available on the UK government website.
Information on scholarships for international students, is available at http://www.qub.ac.uk/International/International-students/International-scholarships/.
How to Apply
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.
Fees and Funding