Data Analytics

Overview

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.



Course Structure

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



Indicative number of modules per semester: 3

Graduate

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.



AICC/NI Cyber funding: A limited number of fully funded places (provided by the Department for the Economy) are available for this programme for eligible applicants resident in Northern Ireland. Applicants are advised to apply as early as possible in order to be considered for a funded place. You will be notified as soon as possible whether your application has been selected for a funded place. If you have not been selected for a funded place, we will accept self-funded or employer-funded applicants, if spaces are available.



In the event that any programme receives a high number of applications, the University reserves the right to close the application portal prior to the deadline stated on course finder. Notifications to this effect will appear on the application portal against the programme application page.

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.



Please note: a deposit will be required to secure a place.

Coursework

Written examination

Project dissertation

1 year (Full-time), 2 years (Part-time).

Entry Year: 2025/26

Career Prospects

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/