**There are a number of fully funded full-time and part-time places available for students resident in or with settled status in Northern Ireland. Funding provided by AICC. All eligible applicants will be considered for funding. Entry requirements and ranking criteria applies. For more information, search 'AI Collaboration Centre QUB'.
Data Analytics is an exciting field of rapid developments. Data is everywhere and continuing to grow massively, creating huge growth in demand for qualified experts to be able to extract the real benefit from the data.
The role of a data scientist is highly diverse overlapping many areas from computer science, to the fundamentals of mathematics, statistics, modelling and analytics while also requiring the right skills to be able to see the detail, solve the problem (having specified the problem!), and communicate effectively the findings to colleagues to empower them to make decisions.
The diversity of data analytics opens up many job opportunities from working in software companies, healthcare, banking, insurance, policing, tech companies to applying your knowledge to intelligent buildings and behaviour analytics of customers.
The programme provides a balanced route to learning through a blend of academic study and lab sessions, with a heavy focus on practical engagement with industry. In the first and second semesters, you will study 6 modules full-time which include opportunities for blended and collaborative learning. In the third semester you will undertake a significant industry based project.
PLEASE NOTE:
You may be required to sit an online aptitude test. The test has been created by the MSc Data Analytics lecturers, and is based on the Cambridge Assessment TMUA; you can find more information on the TMUA, as well as practice papers should you wish to try them, here: https://www.admissionstesting.org/for-test-takers/test-of-mathematics-for-university-admission/preparation/
Aptitude Tests will be held on the dates below 3-4pm (GMT) for 24/25.
10 October 2024
7 November
5 December
9 & 23 January 2025
6 & 20 February
6 & 20 March
10 April
1 & 15 & 29 May
12 & 26 June
**Applicants must attend their allocated session as notified by the school. Please ensure that you check your junk email folders regularly for communications from the School regarding your application/aptitude test. Additional aptitude tests may be scheduled if required.
In addition, a deposit will be required to secure a place. Please see below the deposit deadline schedule:
1. Offer received by 31 December 2023 deposit deadline 31 January 2024
2. Offer received by 31 January 2024 deposit deadline 28 February 2024
3. Offer received by 28 February 2024 deposit deadline 31 March 2024
4. Offer received by 31 March 2024 deposit deadline 30 April 2024
Please note that applicants may be required to undertake an aptitude test.
Decisions will be issued within 10 working days from the date of the aptitude test.
In addition, a deposit will be required to secure a place.
Data Analytics highlights
Student Experience
This course is unique having been developed from engagement with industry rather than the traditional academic subject areas. The key core skills that a data scientist needs have been clearly defined and forms the basis for the course. As a result, there are no optional modules or choice as it is essential that in order to produce the “all rounded” data scientist that all these skills are packaged into each individual.
Industry Links
Special features of the course include the Analytics in Action module and the commitment of industry to provide real data for “Analytathons” and projects. The module offers real world examples of data analytics presented by the industry experts working alongside the academics who will provide the theory, and the unique provision of this course across many academic disciplines in the University.
World Class Facilities
The Frontiers in Analytics module shows off some of latest state-of-the-art techniques analytics in particular in Visual Analytics and Behavioural Analytics.
Modules are taught in block delivery mode where each module runs in blocks of 4 weeks in a sequential manner where at any one time, the student is working on only one module.
Week 1 requires students to carry out background reading and preparation work in advance of
Week 2 requires students to attend lectures/labs Monday – Friday 9am-5pm
Week 3 is normally reserved for project work and helpdesk sessions
Week 4 is normally reserved for additional helpdesk sessions and assessments
Full-time students are expected to be present at Queen’s during each Teaching Week (week 2) of each module as well as Assessment (normally week 4).
In the four week duration of a module, there will be an intensive teaching week where the schedule will consists of 9am-5pm with approximately equal numbers of lectures (in the mornings) and labs (in the afternoons).
Part time students, please note that although the course is part time in terms of number of modules taken each year, the modules themselves are still taught full time in block delivery mode as detailed above.
Course Details
The aim of the programme is to offer a multi-disciplinary education in data analytics that prepares graduates with key knowledge, skills and competencies necessary for employment in analytics and data science positions. In particular, the programme aims to provide students with:
Comprehensive knowledge and understanding of the fundamental principles of statistics and computer science that underpin analytics.
Advanced knowledge and practical skills in the theory and practice of analytics.
The necessary skills, tools and techniques needed to embark on careers in data analytics and data science.
Skills in a range of practices, processes, tools and methods applicable to analytics in commercial and research contexts.
Timely exposure to, and practical experience in, a range of current software packages and emerging new applications of analytics.
Opportunities for the development of practical skills in a commercial context.
Modules
Data Analytics Fundamentals
Databases and Programming Fundamentals
Data Mining
Machine Learning
Frontiers in Data Analytics
Analytics in Action
Individual Industry Based Project
School of Maths and Physics Email: mp.pgt@qub.ac.uk
Learning and Teaching
Students must complete modules in block delivery mode where each module runs in blocks of 4 weeks in a sequential manner where at any one time, the student is working on only one module. Week 1 of block delivery mode requires students to carry out background reading and preparation work in advance of week 2 of each block which requires students to attend lectures/labs Monday –Friday 9am-5pm.
Weeks 3 and 4 of each block are for project and coursework. Full-time students are expected to be present at Queen’s during weeks 2, 6, 10, 14, 18 and 22 of the academic year.
In the four week duration of a module, there will be an intensive teaching week where the schedule will consists of 9am-5pm with approximately equal numbers of lectures (in the mornings) and labs (in the afternoons).
Part time students, please note that although the course is part time in terms of number of modules taken each year, the modules themselves are still taught full time in block delivery mode as detailed above.
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Assessment
Assessments associated with the course are outlined below:
Coursework
Written examination
Project dissertation
Presentations
What our academics say
The MSc Data Analytics will equip successful candidates for any data scientist role and make them highly marketable for what is now seen as “the sexiest job in the 21st century” as reported by The Harvard Business Review.
The information below is intended as an example only, featuring module details for the current year of study (2024/25). Modules are reviewed on an annual basis and may be subject to future changes – revised details will be published through Programme Specifications ahead of each academic year.
The module will provide the basics of how to extract, store, manage, manipulate and integrate both big and small data using Python. The module will also provide the fundamentals of programming, an introduction to procedural programming and object oriented programming, the basic concepts, the differences between the two approaches, their strengths and weaknesses and some practical experience of coding. Topics covered in this module include:
• Data extraction, management, manipulation and integration
• Introduction to SQL
• Procedural programming concepts
• Object oriented programming concepts
Learning Outcomes
Understand fundamental concepts in programming such as variables, loops, logic and functions.
Demonstrate knowledge and understanding of appropriate techniques in Python for building efficient programs.
Demonstrate knowledge and understanding of the scientific Python infrastructure and use modules for data manipulation and visualisation.
Demonstrate knowledge and understanding of applying practical programming and database skills to solve common problems.
Skills
Demonstrate ability to design, develop, test and debug simple programs
This module will introduce the basics of data mining and present the need for data mining approaches and how they can handle big data. Data mining is the study of data to identify new and interesting characteristics generating new information from pre-existing data sets. The following techniques are covered along with their implementation in R.
• Introduction: Definition of data mining, how it has developed and the need for data mining techniques in today’s society;
• Data Reduction/Variable reduction/Dimension reduction methods such as principle component analysis.
• Linear Models and Generalised Linear Models (GLMs): Models for one outcome variable, models with multiple covariates, variable selection methods
• Classification and the classification methods of simple linear, nearest neighbour, decision tree models, Bayes classifying.
• Clustering and be familiar and able to use hierarchical clustering and the non-hierarchical clustering methods of k means and nearest neighbour when applied to real data sets; understand and use association rules and their application on real data sets.
Learning Outcomes
• Comprehensive understanding of the concept of data mining and principal data mining algorithms
• Understand the mathematical representation of the data mining approaches
• Critically evaluate when and why it is suitable to use different algorithms
• Critical awareness of data mining techniques
• Practical techniques in data analytics and mining
Critically reflect on how to implement data mining algorithms, test and apply them to common problems.
Skills
The ability to use statistical data mining to model data, make predictions and recommendations from the models produced.
This module will introduce data analytics and the basic approaches used to collect and investigate data in a meaningful way. The statistical concepts for understanding distributions and probability will be introduced along with a number of tests and approaches that can be used to evaluate the quality of data assessing it for blunders, missingness, outliers and skewness. Statistical models and the concept of predictive analytics will be introduced and examples given through the introduction of regression analysis. The module will introduce the R software. Topics covered will include:
Introduction to data analytics with examples of analytics from companies such as Facebook and Netflix.
Hypothesis testing for determining the significance of observations.
Statistical modelling and predictive analytics
Regression analysis - predicting new data values via regression models.
R system for working with statistical data.
Learning Outcomes
On completion of this module, a student will have achieved the following learning outcomes, commensurate with module classification:
Knowledge and understanding of the concept of data analytics and predictive analytics.
Knowledge and understanding of hypothesis testing.
Be able to carry out predictive analytics using regression analysis.
The ability to carry out analysis using the R package.
Skills
The ability to use statistical tools to assess data quality and distributional form and cleanse data.
This module will introduce the basics of machine learning algorithms and how to achieve practical implementations of the core methods. This will include a study of classical machine learning algorithms; supervised and unsupervised methods; applications and tools. It will also cover modern methods including deep learning and convolutional neural networks. The module will provide a basic understanding of application areas including implementation in Python. Topics covered will include:
Unsupervised methods
o Self-organising maps, EM,
o Dimensionality reduction: PCA, LDA
Supervised methods
o K-NN, decision trees, boosting
o Ensemble methods, random forests
o Neural networks
o Deep learning, CNNs (introduction)
Applications
o Text mining and information retrieval
o Active learning
o Vision: Face recognition
o Genetic algorithms
Tools
o Python, Matlab
R
Learning Outcomes
• Comprehensive understanding of principal machine learning algorithms
• Critically evaluate when and why it is suitable to use different algorithms
• Critical awareness of machine learning techniques
• Practical techniques in data analytics, mining and visual analytics
Critically reflect on how to implement machine learning algorithms, test and apply them to common problems such as text mining, image analysis, vision and time series modelling.
Skills
The ability to use machine learning algorithms to make predictions on real data.
The module highlights two state-of-the-art disciplines in the general field of analytics: Visual Analytics and Behavioural Analytics. Both disciplines include exploration of how humans are involved analytics, albeit from very different perspectives.
This module will firstly introduce the concept of Visual Analytics, defined as the science of analytical reasoning that is facilitated by interactive visual interfaces, and its role in decision making for complex systems. The definition of visual analytics will be discussed in detail, and a summary of its integral components explored, including analytical reasoning, data representations and transformations. The module will include introduction to the design and development of interactive dashboards, along with practical implementation. Decision theory and decision making will also be addressed.
Secondly the module will introduce the basics of Behavioural Measurement and Analytics exploring the key issues involved in measuring and analysing human behaviour. The module addresses the opportunities, difficulties and issues that arise from the use of multiple sensor technologies, wearable and small scale devices. It explores the use of these technologies to measure and understand human behaviour; mood feelings and emotions; social interactions and communication; and movement in different settings.
Module topics covered will include:
• Visual Analytics as a science
• Decision theory and decision making
• Surrogate modelling
• Visualization theory and techniques (could take this one out if too many bullets)
• Creation of interactive decision-making environments, including dashboards
• Affective Computing and Social Signal Processing
• Synchronisation and multi-modal behaviour measurement
• Face and Facial Expression Recognition
• Auditory behaviour, Language and Sentiment Analysis
Biometric Measurement, body movement and motion capture
Learning Outcomes
• Comprehensively describe visual analytics as a science
• Assess and interpret large, disparate data sets
• Design and create bespoke interactive decision-making environments
• Comprehensive knowledge of behavioural measurement and analytics, affective computing and social signal processing.
• A theoretical understanding and an ability to assess and be aware of the challenges that arise within and between analysis in various behavioural modalities
A practical ability to address behaviour analytics problems in one or more modalities.
Real data presents new challenges. Unlike the well “behaved” data often used as illustrative examples in text books, real data will require careful attention to ensure it is used correctly and that the correct approaches are applied to it. When engaging with a client in a company, the analytics expert may be the only analytics person in the team so be required to know the question being asked in terms of the analytics required and articulate the results of the analysis of this professionally to a non analytics expert audience. The purpose of this module is to provide experience of working with real-life data, to gain knowledge of analytics projects in industry and research, and to act as training for the individual industry placement module. The problems that will be presented may be open-ended, and in many cases will require the student to set up the problem in analytics terms before attempting its solution.
Learning Outcomes
Translation of real data into meaningful knowledge using analytics.
Deep understanding of analytics to be able to select the most appropriate method for the situation being presented.
Effective presentation and translation of the analytics results and impact on business to both experts and to non-experts.
Develop complex supporting code and analytical model for the data.
Critically analyse results
Communicate conclusions clearly
Skills
Successful participation in this module will enable students to develop skills in the following areas:
To evaluate a real life problem and make an informed judgement as to the most suitable analytics approach to take.
To implement the analytics approaches using real data.
To effectively present the results and recommendations of the analysis to expert and non-expert colleagues.
The project will take the form of an extensive analytics investigation and be based in a local company. Each project will involve aspects of the data analytics journey from data cleansing, data manipulation, to analysis, mining, visual analytics to developing a piece of code, using R packages or other software and applying the theory of the previous modules or that in the literature to real data. The results from the investigation will be analysed and appropriate conclusions drawn.
Learning Outcomes
On completion of this module, the successful student will have achieved the following learning outcomes, commensurate with module classification:
• Deep knowledge and understanding of a given problem.
• Critically evaluate an analytics problem.
• Conduct a detailed analysis of the literature or previous case studies.
• Act autonomously and creatively in planning and implementing tasks.
• Develop complex supporting code and analytical model for the data.
• Critically analyse results
Communicate conclusions clearly
Skills
Successful completion of this project module will enable students to develop skills in the following areas:
To evaluate a real life problem and make an informed judgement as to the most suitable analytics approach to take.
To implement the analytics approaches using real data.
To effectively present the results and recommendations of the analysis to expert and non-expert colleagues.
Normally a 2.1 Honours degree in Mathematics, Statistics, or Computer Science or a closely related discipline, or equivalent qualification acceptable to the University.
Applicants with a minimum 2.2 Honours degree in a cognate discipline, a 2.1 Honours degree in a non-cognate discipline, or who have not yet completed their degree, will be required to pass an aptitude test.
Please note the closing date for MSc Data Analytics for September 2025 entry is 31 March 2025 at 4pm. Applications received after this date and time will be regarded as LATE and will be considered only if vacancies exist when all applications received by this date and time have been processed.
We encourage applicants to apply as early as possible. In the event that any programme receives a high number of applications, the University reserves the right to close the application portal earlier than 31 March 2025 deadline. Notifications to this effect will appear on the Direct Application Portal against the programme application page.
Please note: a deposit will be required to secure a place.
International Students
Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region. Use the dropdown list below for specific information for your country/region.
English Language Requirements
Evidence of an IELTS* score of 6.5, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required.
*taken within the last 2 years
International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
Academic English: an intensive English language and study skills course for successful university study at degree level
Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.
Industry forecasts indicate that Data Analytics is a growing field internationally, with job opportunities set to increase exponentially predicting growths of 160% between 2013 and 2020 (eSkills report, Big Data Analytics 2013-2020). There is a current shortage in qualified staff for these roles, which is also the case in Northern Ireland where there have been a number of recent investments and expansions in the Data Analytics sector.
The course is designed to meet the needs of Industry where graduates have the right combination of the skills and expertise in both computer science, mathematics and statistics along with the experience they gain in their individual industry based project to be highly sought after for employment.
Queen's postgraduates reap exceptional benefits. Unique initiatives, such as the leadership and executive programmes alongside sterling integration with business experts helps our students gain key leadership positions both nationally and internationally. http://www.qub.ac.uk/directorates/sgc/careers/
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.
1EU 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 relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.
Terms and Conditions for Postgraduate applications:
1.1 Due to high demand, there is a deadline for applications.
1.2 You will be required to pay a deposit to secure your place on the course.
1.3 This condition of offer is in addition to any academic or English language requirements.
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.
How do I fund my study?
The Department for the Economy will provide a tuition fee loan of up to £6,500 per NI / EU student for postgraduate study. Tuition fee loan information.
A postgraduate loans system in the UK offers government-backed student loans of up to £11,836 for taught and research Masters courses in all subject areas (excluding Initial Teacher Education/PGCE, where undergraduate student finance is available). Criteria, eligibility, repayment and application information are available on the UK government website.