Programme Specification
MSc Business Analytics
Academic Year 2022/23
A programme specification is required for any programme on which a student may be registered. All programmes of the University are subject to the University's Quality Assurance processes. All degrees are awarded by Queen's University Belfast.
Programme Title | MSc Business Analytics | Final Award (exit route if applicable for Postgraduate Taught Programmes) |
Master of Science | |||||||||||
Programme Code | MGT-MSC-BU | UCAS Code | HECoS Code |
100360 - Business computing - 100 |
ATAS Clearance Required | No | |||||||||||||
Mode of Study | Full Time | |||||||||||||
Type of Programme | Postgraduate | Length of Programme |
Full Time - 1 Academic Year |
Total Credits for Programme | 180 | |||||||||
Exit Awards available | No |
Institute Information
Teaching Institution |
Queen's University Belfast |
School/Department |
Queen's Business School |
Quality Code Higher Education Credit Framework for England |
Level 7 |
Subject Benchmark Statements The Frameworks for Higher Education Qualifications of UK Degree-Awarding Bodies |
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Accreditations (PSRB) |
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No accreditations (PSRB) found. |
Regulation Information
Does the Programme have any approved exemptions from the University General Regulations |
Programme Specific Regulations The MSc Business Analytics will be subject to the guidelines presented in the Study Regulations for Postgraduate Taught Programmes. |
Students with protected characteristics |
Are students subject to Fitness to Practise Regulations (Please see General Regulations) No |
Educational Aims Of Programme
•foster a stimulating and supportive learning environment which promotes intellectual, professional and personal development
•encourage critical thinking, independent enquiry, and an international outlook
•develop the skills necessary to undertake independent research and continuing professional development
•develop students' skills base, leadership capacity and connections with practice in ways which will enhance their ability to make valuable contributions to the economy and society
•promote engagement with issues of ethics, responsibility and sustainability, and maintain respect for social and cultural differences and openness, fairness, and equality of opportunity in relation to selection, learning, assessment, and support
•Develop student’s statistical skills as they pertain to business analytics. Students will gain skills in areas of core statistics, such as descriptive statistics, probability, hypothesis testing, and regression.
•Develop students computing and IT skills as they pertain to business analytics. Specifically, students will develop skills in programming, machine learning, data visualisation, and data management.
•Develop student’s business skills as they pertain to business analytics. Students will gain knowledge of core business functions, as well as the application of analytics to solve complex business problems.
Other educational aims include:
•To develop strengths in analysing, synthesising and solving complex unstructured business problems using analytics.
•To develop skills in communicating analytics outputs and to interact effectively within and outside of the organisation.
Learning Outcomes
Learning Outcomes: Cognitive SkillsOn the completion of this course successful students will be able to: |
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Problem solve |
Teaching/Learning Methods and Strategies A variety of teaching and learning methods are used throughout the modules listed below including lectures, computer lab sessions, national and international case studies, and directed reading. Methods of Assessment Student’s problem solving skills will be tested throughout the course, but specifically in the final dissertation module where they will be required to develop a solution to a business problem. |
Reason logically |
Teaching/Learning Methods and Strategies A variety of teaching and learning methods are used throughout the modules listed below including lectures, computer lab sessions, national and international case studies, and directed reading. Methods of Assessment Student’s problem solving skills will be tested throughout the course, but specifically in the final dissertation module where they will be required to develop a solution to a business problem. |
Conduct independent enquiry |
Teaching/Learning Methods and Strategies Due to the fast pace of change in the analytics industry, student’s ability to conduct independent learning and enquiry is crucial to their future success. Methods of Assessment Most of the assessments will require students to conduct independent inquiry. |
Critically evaluate and interpret |
Teaching/Learning Methods and Strategies Students will learn to critically evaluate theories, research findings, analytical tools, solutions, and analytical techniques. Methods of Assessment All assessments will require students to use critical evaluation, interpretation, and creativity. |
Self-assess and reflect |
Teaching/Learning Methods and Strategies Students will learn to evaluate their own strengths and weaknesses, which will feed into their motivation for self learning. The course will overview many tools and techniques, and not all in detail – students can take this knowledge to engage in self-directed learning. Methods of Assessment Students will be required to reflect on their own skills and abilities throughout the course, and will be encouraged to work on any gaps in their own analytics and business skills through both the core course material and self directed learning. Students will also be required to reflect on their learning during other modules. |
Learning Outcomes: Knowledge & UnderstandingOn the completion of this course successful students will be able to: |
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Explain the structure and functions of organisations, their external context, and management, and in particular, how analytics fits into this landscape. |
Teaching/Learning Methods and Strategies Modules are offered which incorporate functional areas of the organisation such as marketing, human resources, and operations management. Methods of Assessment Students will be required to demonstrate their understanding through a range of assessment methods including written assignments, presentations, and group projects. |
Evaluate and apply analytical techniques to solve a range of business problems and to make business decisions. |
Teaching/Learning Methods and Strategies This will be embedded throughout the technical modules and the functional area modules. Methods of Assessment Students will be required to solve business analytics problems throughout the assessments. This will include written problem solving, for example, in essays, and technical problem solving in group work, assignments, and the final dissertation. |
Evaluate and apply analytical techniques in the development of new and improved products and processes. |
Teaching/Learning Methods and Strategies Students will be challenged throughout the course to think about new products and processes that can be developed using analytics. Methods of Assessment Students will be required to demonstrate the application of business analytics to the development of new products and processes both technically through group work, assignments, and the final dissertation. |
Critically evaluate and develop solutions using industry standard analytics tools and techniques to include data management, machine learning, and data products. |
Teaching/Learning Methods and Strategies Training on analytics tools will be embedded throughout the course – in particular, students will use cutting edge machine learning and visualisation tools in the modules on statistics, machine learning and building data products. Industry standard storage and processing tools will be covered in the module on data management. Students will take the marketing analytics module will also have the opportunity to participate in SAS training. Methods of Assessment Students will be required to use industry standard tools in the practical assessments and dissertation to implement business analytics solutions. |
Evaluate, apply, and interpret the output from statistical techniques, and their application in business analytics. |
Teaching/Learning Methods and Strategies Core statistical concepts will be covered in a dedicated statistics module. The application of statistics in analytics will be covered in the machine learning module. Methods of Assessment Students will be required to demonstrate their statistical ability in the modules Statistics for business, and in Advanced analytics and machine learning. These skills will be assessed through assignments, as well as the final dissertation. |
Describe and develop solutions using core computing topics as they pertain to business analytics, and the ability to use computing skills to solve business analytics problems independently and in collaboration with other stakeholders |
Teaching/Learning Methods and Strategies Students will develop the ability to solve computing problems independently e.g. through effective use of online support forums, as well as understanding when a problem is outside of their expertise. Methods of Assessment Core computing skills will be assessed through the assignments and the final dissertation. Much of the assessment will involve problem solving, where students will have to work independently and in groups to develop technical business analytics solutions. |
appreciate diversity and be capable of placing issues within their local and international contexts |
Teaching/Learning Methods and Strategies All modules will consider both local and international and diverse companies and business analytics applications. Methods of Assessment Students will focus on applications which incorporate diversity and have international reach. |
engage with issues around ethics, responsibility and sustainability |
Teaching/Learning Methods and Strategies All modules will consider issues around ethics, responsibility and sustainability. The AI in Business and Society module in particular will focus heavily on this area. Methods of Assessment Assignments will incorporate consideration of ethics, responsibility, and sustainability. In particular, the AI in Business and Society module assignments will focus in depth on these issues |
Learning Outcomes: Subject SpecificOn the completion of this course successful students will be able to: |
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Critically evaluate business problems and identify analytics driven solutions. |
Teaching/Learning Methods and Strategies Students will be challenged to evaluate current analytics solutions through national and international case studies of leading companies, as well as developing solutions to business analytics challenges using real world data. This will be embedded throughout all of the mandatory modules. Methods of Assessment This will be assessed throughout the modules through individual and group assignments, and the final dissertation. |
Critically evaluate the role of business analytics within organisations, and in particular its role in operational and strategic decision making. |
Teaching/Learning Methods and Strategies This is embedded particularly in the modules on statistics, machine learning, and data product development. Methods of Assessment Assignments will challenge students to frame solutions in the context of the wider organisation and external environment. |
Solve business problems through the management and processing of big and small datasets. |
Teaching/Learning Methods and Strategies This core skill will be embedded throughout the core modules, as well as the dissertation. In depth understanding, and practical skills, will be developed particularly in the data management module. These skills will be applied, and necessary, to complete the final dissertation. Methods of Assessment All of the core modules will include assignments focusing on analysing data, which includes the ability to manage and process the data before and during the application of analytical techniques. |
Evaluate, apply, and interpret output from machine learning and other statistical models to solve business problems. |
Teaching/Learning Methods and Strategies Basic statistics and machine learning will be explored in the statistics module, before students progress to a more in depth exploration of machine learning and advanced analytics in the dedicated module. Students will work with real world data to solve complex business problems in these modules. Methods of Assessment Assignments in the statistics for business and advanced analytics modules. |
Evaluate, apply, and interpret output from decision making tools and techniques, including advanced data visualisation. |
Teaching/Learning Methods and Strategies Data visualisation is one of the first steps in any business analytics project. Students will therefore undertake exploratory visualisation in all of the core modules, but will explore this in detail in data driven decision making. This will be one of the core features of the dissertation project. Methods of Assessment Group and individual assignments in the data driven decision making module, and more generally through assignments on the statistics, and advanced analytics modules. |
Critically evaluate the wider ethical and societal implications of business analytics from both a national and international perspective. |
Teaching/Learning Methods and Strategies Students will consider the wider implications of analytics during case study and group discussions. Ethical discussions on the use of data will form part of the core modules. A particular focus on ethics will be included in the Artificial Intelligence in Business and Society module. Methods of Assessment Students will be required to consider the wider implications of analytics during their assignments. |
Learning Outcomes: Transferable SkillsOn the completion of this course successful students will be able to: |
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Confidently engage with the world of practice |
Teaching/Learning Methods and Strategies All modules will involve consideration of the practical and business side of real world business analytics applications. Methods of Assessment The majority of assessments are project based, and involve building real world business analytics applications. |
Work both independently and in groups |
Teaching/Learning Methods and Strategies Students will have opportunities to work both independently and in groups. This includes developing solutions individually and in groups. Methods of Assessment Modules include both individual and group work and assessment. |
Organise and manage their time |
Teaching/Learning Methods and Strategies Students will be required to manage their time effectively through group and individual assignments, and more generally through independent study, attending lectures etc. Methods of Assessment The planning and delivery of technical solutions will form components of the group and individual assignments. |
Synthesise and evaluate information/data from a variety of sources including from databases, books, journal articles and the internet |
Teaching/Learning Methods and Strategies Students will receive instruction of acquisition of information, for example, through the data management module. Methods of Assessment Most assessments will require students to synthesise written material, particularly the less technical modules and those focusing on specific business domains. |
Communicate ideas in both written and presentational forms |
Teaching/Learning Methods and Strategies Class sessions will allow students to communicate complex concepts, for example through presentations and group discussions. The data decision making module in particular will require students to communicate the rational for particular decisions, for example through data storytelling. Methods of Assessment Assessment of written communication, and presentation of solutions. |
Make effective use of information technology including relevant subject-specific packages |
Teaching/Learning Methods and Strategies Technical and analytical classes will involve the use of a range of software packages and programming languages to carry out tasks such as statistical analysis, machine learning, data wrangling, data visualisation, and data storage. Methods of Assessment Individual and group assessments across will require programming and software skills. |
Module Information
Stages and Modules
Module Title | Module Code | Level/ stage | Credits | Availability |
Duration | Pre-requisite | Assessment |
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S1 | S2 | Core | Option | Coursework % | Practical % | Examination % | ||||||
Marketing Analytics | MGT7215 | 7 | 15 | -- | YES | 15 weeks | N | YES | -- | 100% | 0% | 0% |
Data Mining | MGT7216 | 7 | 15 | -- | YES | 15 weeks | N | YES | -- | 100% | 0% | 0% |
MSc Business Analytics Dissertation Portfolio | MGT7219 | 7 | 60 | -- | YES | 15 weeks | N | YES | -- | 100% | 0% | 0% |
Human Resources Analytics | MGT7182 | 7 | 15 | YES | -- | 15 weeks | N | YES | -- | 100% | 0% | 0% |
Artificial Intelligence in Business and Society | MGT7181 | 7 | 15 | YES | -- | 15 weeks | N | YES | -- | 100% | 0% | 0% |
Data Management | MGT7178 | 7 | 15 | YES | -- | 15 weeks | N | YES | -- | 100% | 0% | 0% |
Advanced Analytics & Machine Learning | MGT7179 | 7 | 15 | -- | YES | 15 weeks | N | YES | -- | 100% | 0% | 0% |
Data Driven Decision Making | MGT7180 | 7 | 15 | -- | YES | 15 weeks | N | YES | -- | 100% | 0% | 0% |
Statistics for Business | MGT7177 | 7 | 15 | YES | -- | 15 weeks | N | YES | -- | 100% | 0% | 0% |
Notes
Students must normally have successfully completed all taught courses of at least 105 CATS before progressing to the dissertation portfolio stage of the programme.