Data Science

Overview

Data Science is a rapidly developing field of study within both academia and industry. Its interdisciplinary nature ensures its wide application domain. This MSc Data Science aims to prepare students for a successful career as a data scientist or business analyst, working in any profession where large amounts of datare collected, and there is a need for skills in data acquisition, information extraction, aggregation and representation, and data analysis using state-of-the-art machine learning techniques. These skills are typically in high demand in many industries including IT, business, security, health, intelligent transport, energy, and the creative industries.



More generally data and analytics capabilities have developed rapidly in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. Most companies, however, are not capturing the full potential value from data and analytics because they do not have the required expertise. Consequently, the MSc Data Science aims to address these challenges by providing a firm grounding in the core disciplines of data analytics and information processing, partnered with a broad appreciation of aspects of other disciplines where data science can form natural synergistic relationships.

Modules:

Data Science Foundations

Big Data Technologies

Business Intelligence and Analytics

Data Validation and Visualisation

Deep Learning and Natural Language Programming

Statistical Modelling and Machine Learning

Research Masters Project (Applied Research)

Applicants must:

(a) have gained



(i) a second class honours degree or better, in the subject areas of computing, engineering or related discipline, from a university of the United Kingdom or the Republic of Ireland, or from a recognised national awarding body, or from an institution of another country which has been recognised as being of an equivalent standard; or



(ii) an equivalent standard (normally 50%) in a Graduate Diploma, Graduate Certificate, Postgraduate Certificate or Postgraduate Diploma or an approved alternative qualification; and the qualification must be in the subject areas of computing, engineering or related discipline



and



(b) provide evidence of competence in written and spoken English (GCSE grade C or equivalent).



In exceptional circumstances, as an alternative to (a) (i) or (a) (ii) and/or (b), where an individual has substantial and significant experiential learning, a portfolio of written evidence demonstrating the meeting of graduate qualities (including subject-specific outcomes, as determined by the Course Committee) may be considered as an alternative entrance route. Evidence used to demonstrate graduate qualities may not be used for exemption against modules within the programme.



English Language Requirements

English language requirements for international applicants

The minimum requirement for this course is Academic IELTS 6.0 with no band score less than 5.5. Trinity ISE: Pass at level III also meets this requirement for Tier 4 visa purposes.



Ulster recognises a number of other English language tests and comparable IELTS equivalent scores.



• For English language requirements go to: https://www.ulster.ac.uk/global/apply/english-language-requirements



• For Your country go to: https://www.ulster.ac.uk/global/apply/country



Exemptions and transferability

The entry requirements facilitate accreditation of prior learning.

Your Application

You can apply directly to Ulster at any time of year. We advise that you submit your application no later than two weeks prior to your course start date. This is to ensure there is sufficient time for your application to be reviewed and, should you be made an offer, time for you to complete all the necessary pre-enrolment tasks.



Some programmes will have specific deadlines as additional selection (e.g. interview, portfolio submission etc) is required, so please check the course page to ensure you have sufficient time to complete your application.

The full-time provision offers two points of entry in each academic year in September and January. For the September intake, the degree will normally be completed in three semesters across a single academic year. For the January intake, the degree will normally be completed in three semesters but across two academic years. The programme is delivered in-person at the Derry~Londonderry campus. It is not available in distance learning/online delivery mode.



The part-time provision offers two points of entry in each academic year in September and January. The six taught modules are delivered across the first four semesters, which run across two years. The project is normally completed in the third year. For September intake, the project may be 'fast-tracked' to start in the summer of year 2 (if desired). The programme is delivered in-person at the Derry~Londonderry campus. It is not available in distance learning/online delivery mode.

Start Date: September 2026 and January 2027.

Careers and Opportunities

The key message from employability and work-related learning initiatives is that enhancing opportunities to develop work-related learning and employability enhances the learning of the subject being studied. We understand the importance of including real industrial and commercial contexts to our student's experience, so this MSc Data Science will pursue opportunities for industrially linked teaching material and student project work. In this regard, we will utilise our business and industry links to facilitate an industrially relevant student project. Such projects create valuable experiences for the student, and additionally, they can also help to build new and ongoing collaborations with departments and companies, with the potential to tap into funding streams designed for industry-academic research and development.



A recent statement from Ulster University’s Careers Office indicates that Data analysts are in high demand across all sectors, such as finance, consulting, manufacturing, pharmaceuticals, government and education. Data analysts can work in large companies such as the ‘big four’ consultancies or financial services firms, or consumer retail firms, small and medium sized businesses such as marketing agencies’ or the public sector.