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 Correlation Probability Distributions 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.

Career Prospects

Introduction
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

Assessment

Assessments associated with the course are outlined below:

Modules

• Year 1

  Core Modules

  Marketing Analytics (15 credits)

  ×

  Marketing Analytics

  Overview

  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

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  MGT7215

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Data Mining (15 credits)

  ×

  Data Mining

  Overview

  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

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  MGT7216

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  MSc Business Analytics Dissertation Portfolio (60 credits)

  ×

  MSc Business Analytics Dissertation Portfolio

  Overview

  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

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  60

  Module Code

  MGT7219

  Teaching Period

  Summer

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Human Resources Analytics (15 credits)

  ×

  Human Resources Analytics

  Overview

  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

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  MGT7182

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Artificial Intelligence in Business and Society (15 credits)

  ×

  Artificial Intelligence in Business and Society

  Overview

  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

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  MGT7181

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Data Management (15 credits)

  ×

  Data Management

  Overview

  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

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  MGT7178

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Advanced Analytics & Machine Learning (15 credits)

  ×

  Advanced Analytics & Machine Learning

  Overview

  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

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  MGT7179

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Data Driven Decision Making (15 credits)

  ×

  Data Driven Decision Making

  Overview

  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

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  MGT7180

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Statistics for Business (15 credits)

  ×

  Statistics for Business

  Overview

  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

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  MGT7177

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

Entrance requirements

Graduate
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

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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.

Career Prospects

Introduction
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.

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Fees and Funding

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 to Apply

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

Apply now

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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