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MSc Business Analytics

Academic Year 2021/22

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 and Enhancement processes as set out in the DASA Policies and Procedures Manual.

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

ATAS Clearance Required

No

Health Check Required

No

Portfolio Required

Interview Required

Mode of Study

Full Time

Type of Programme

Postgraduate

Length of Programme

1 Academic Year(s)

Total Credits for Programme

180

Exit Awards available

INSTITUTE INFORMATION

Teaching Institution

Queen's University Belfast

School/Department

Queen's Management School

Quality Code
https://www.qaa.ac.uk/quality-code

Higher Education Credit Framework for England
https://www.qaa.ac.uk/quality-code/higher-education-credit-framework-for-england

Level 7

Subject Benchmark Statements
https://www.qaa.ac.uk/quality-code/subject-benchmark-statements

The Frameworks for Higher Education Qualifications of UK Degree-Awarding Bodies
https://www.qaa.ac.uk/docs/qaa/quality-code/qualifications-frameworks.pdf

Accreditations (PSRB)

REGULATION INFORMATION

Does the Programme have any approved exemptions from the University General Regulations
(Please see General Regulations)

Programme Specific Regulations

The MSc Business Analytics will be subject to the guidelines presented in the Study Regulations for Postgraduate Taught Programmes.
The MSc Business Analytics is based on the University wide modular framework. The class of degree awarded to the student (Fail, Pass,
Commendation and Distinction) is based on his or her performance in 8 compulsory academic modules and the dissertation. Marking is based on the University's agreed making scale.
Programme Specific Regulations
Students who pass all the taught modules, but who
fail to achieve a mark of at least 50% in the dissertation shall be eligible for the award of Postgraduate Diploma only.
Students who pass all the taught modules but who fail to submit a dissertation shall be eligible for the award of Postgraduate Diploma only.
Students who pass modules worth at least 60 CATS will be eligible for the award of Postgraduate Certificate.

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 Skills

On the completion of this course successful students will be able to:

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.

The analytics modules have an applied focus to ensure the theory relates to real world analytics business problems.

Teaching methods focused on problem solving using analytics include national and international case studies, and the analysis of real world data to solve problems.

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.

A variety of assessment methods are used including individual and group coursework, and presentations.

Both formative and summative methods are used.

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.

The analytics modules have an applied focus to ensure the theory relates to real world analytics business problems.

Teaching methods focused on problem solving using analytics include national and international case studies, and the analysis of real world data to solve problems.

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.

A variety of assessment methods are used including individual and group coursework, and presentations.

Both formative and summative methods are used.

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.

Students will be expected to solve complex problems using both the material taught, and through the material they have learned independently.

Specifically, students will be guided in how to find and analyse other sources of information (e.g. books, journal articles). Students will also learn how to solve technical problems independently through the use of online resources.

Students will also be provided with suggestions for further reading and learning, which they can draw on to enhance their skills and employability.

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 & Understanding

On the completion of this course successful students will be able to:

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.

Students will engage in case studies to build technical solutions to real world business problems.

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.

Students will have the opportunity to build an end to end solution in the dissertation module, as well as multiple smaller case study type projects in the mandatory modules.

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.

Students will integrate this knowledge in the final dissertation in building a solution to an analytics problem – including data storage and management, machine learning, and visualisation.

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.

Students will bring this knowledge together in the final dissertation project, where they will apply statistics and machine learning to solve a real world problem.

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.

Core computing skills will be developed throughout the modules, culminating in their application in the dissertation module where students will have to build a technical solution. The induction week will cover core computing concepts. This will allow students to be more effective in controlling and utilising the full capabilities of the machine in business analytics tasks.

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 Specific

On the completion of this course successful students will be able to:

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.

Students should also consider the role of analytics in specific business functions when taking modules in Human Resource Analytics, Marketing Analytics, and Operations Management.

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 Skills

On the completion of this course successful students will be able to:

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

Programme Requirements

Module Title

Module Code

Level/ stage

Credits

Availability

Duration

Pre-requisite

 

Assessment

 

 

 

 

S1

S2

 

 

Core

Option

Coursework %

Practical %

Examination %

Marketing Analytics

MGT7160

7

15

YES

15 weeks

N

YES

100%

0%

0%

Statistics for Business

MGT7177

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%

Artificial Intelligence in Business and Society

MGT7181

7

15

YES

15 weeks

N

YES

100%

0%

0%

Human Resources Analytics

MGT7182

7

15

YES

15 weeks

N

YES

100%

0%

0%

Dissertation MSc Business Analytics

MGT9204

7

60

YES

15 weeks

N

YES

100%

0%

0%

Operations Management

MGT7184

7

15

YES

15 weeks

N

YES

100%

0%

0%

Notes

Due to the impact of the COVID-19 outbreak, the structure of this programme of study has altered for academic year 2020-21 and your module choice may run in a different sequence. You should refer to the programme handbook / your School for specific module details or any queries.

Students must normally have successfully completed all taught courses of at least 105 CATS before progressing to the dissertation stage of the programme.

For Students who commenced the the programme in January 2021 the module code for Artificial Intelligence in Business and Society will be MGT7207 and will be delivered in Semester 1 (Sept 2021).