MSc Data Science
About
This specialist master’s course in Data Science is aimed at highly-motivated graduates with a good honours degree in computing, engineering or a related discipline. While the course has a particular focus on the employment needs of the local economy, the skills and abilities developed are easily transferred to a more global stage.
A major challenge for companies is attracting and retaining the right talent—not only data scientists but business translators who combine data savvy with industry and functional expertise. The science of extracting information from data continues to increase in importance in various disciplines in which the large volume and complexity of the data imposes unprecedented challenges to the data analysis approaches traditionally employed in these disciplines. This course enables graduates to embark on a professional career in the general area of data science with the high level data analytics skills needed to contribute to this rapidly changing marketplace.
The course is accredited by BCS, The Chartered Institute for IT, for Partial CITP (Chartered IT Professional) and Partial CEng (Chartered Engineer). The course is also accredited by The Institute of Analytics (IoA) which is the Professional Body for Analytics and Data Science Professionals worldwide.
Mode
This course is offered in Full-Time and Part-Time mode and is campus-based with blended learning.
Campus
Derry~Londonderry
Start Date
September 2025 (Full-Time and Part-Time)
January 2026 (Full-Time and Part-Time)
Duration
Full-Time: Three semesters over 1 academic year
Part-Time: Six semesters over 3 academic years
Delivery
The MSc Data Science consists of six 20-credit taught modules and one 60-credit Research Project module.
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 part-time provision also offers two points of entry in each academic year in September and January. The taught modules are delivered across two semesters during each of the first two years. Three taught modules are normally completed in each of the first two years (six in total). The project is normally completed in the third year.
Attendance
Each module has 5 hours per week of timetabled classes, typically starting mid-afternoon on a weekday to include lectures and practical classes.
Teaching, Learning and Assessment
Teaching is delivered through lectures and practical sessions. The course is assessed by 100% coursework.
Career Options
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.
Contact us
If you have any queries about the course, please email us: sceis@ulster.ac.uk
Modules
The MSc Data Science award consists of six compulsory taught modules (totalling 120 credits) as well as a Masters Research Project (60 credits).
Data Science Foundations (20 credit points)
The focus of this module is to present an understanding of key data science concepts, tools and programming techniques. Within the arena of data science, the theory behind the approaches of statistics, modelling and machine learning will be introduced emphasising their importance and application to data analysis. The notion of investigative and research skills will also be introduced through a number of problem-solving exercises. The material covered will be contextualised by providing examples of the latest research within the area. Students will also be introduced to programming with Python. They will learn the basics of syntax, and how to configure their development environment for the implementation and testing of algorithms related to data science.
Big Data Technologies (20 credit points)
Within this module a variety of database and data storage paradigms will be explored, ranging from more traditional relational systems to NoSql and object stores, time series databases and graph stores. Consideration will be given to big data and the problem with storing and querying high volumes of highly variable data which is stored and processed at a high speed. The cloud computing paradigm will also be introduced and how to avail of its power and resources.
The core concepts of distributed computing will be examined in the context of Hadoop and Spark. Students will be taught, practically and theoretically, about the components of Hadoop and Spark workflows, functional programming concepts and use of MapReduce.
Business Intelligence and Analytics (20 credit points)
This module aims to contextualise the role of Business Intelligence and Business Analytics and why we need them. A particular focus will be on how to turn already stored data into valuable information and why this is important. For instance, vast amounts of data regarding company's customers and operations is routinely collected and stored in large corporate data warehouses. This data can be of immense value if properly analysed.
Students will explore techniques and tools for data analysis, and presentation of the results to non-technical and managerial staff, in alignment with business strategies. Business intelligence and analytics however, are open to certain ethical and consent issues along with risks. These will be analysed, reviewed and evaluated.
Data Validation and Visualisation (20 credit points)
High-quality data is the precondition for analysing and using big data and for guaranteeing the value of the data. This module, introduces the data quality challenges faced by big data. It will present tools and techniques employed to ensure data quality from data collection and computational procedures to facilitate automatic or semi-automatic identification and elimination of errors in large datasets.
The module also introduces the topic of understanding and interpreting data through descriptive statistical methods. This will be achieved through a range of techniques such as Statistical metrics, Univariate analysis and Multivariate analysis. Students will develop the knowledge to assess the quality of the data and the skills necessary to perform appropriate data cleaning operations. In addition, students will have an understanding of processing data and interpreting and visualising results.
Deep Learning and Natural Language Programming (20 credit points)
Deep Learning and Natural language processing (NLP) are some of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc.
There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. In this course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP.
Topics covered will include word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some very novel models involving a memory component. Students will learn the necessary engineering tricks for making neural networks work on practical problems.
Statistical Modelling and Machine Learning (20 credit points)
This module first provides a systematic understanding to probability and statistics. It then provides an in depth analysis of the statistical modelling process and how to answer hypothesised questions. Next, the module provides a synthesis of the concepts of data mining and methods of exploring data.
The content will be delivered and experienced through lectures, seminars and practical exercises using tools, such as, Python, R and Weka. On completing this module, students will be able to compute conditional probabilities and use null hypothesis significance testing to test the significance of results, and understand and compute statistical measures such as the p-value for these tests. Students will apply, evaluate and critically appraise this knowledge in a range of complex real world contexts.
Research Masters Project (60 credit points)
The aim of the project is to allow the student to demonstrate their ability in undertaking an independent research project for developing theoretical perspectives, addressing research questions using data, or analysing and developing real world solutions. They will be expected to utilise appropriate methodologies and demonstrate the skills gained earlier in the course when implementing the project.
As part of the project development activity they will be required to extract and demonstrate knowledge from the literature in an analytic manner and develop ideas. This may involve the collection of primary or secondary data and the qualitative or quantitative analysis of the data and/ or current industrial process. In summary the masters project represents a piece of work performed by the student under suitable staff supervision which draws both from the practical and creative nature of a problem-solving project and the traditional, scholarly exposition of an area of study. The content of the work must be original and contain a critical appraisal of the subject area.
Eligibility Criteria
Academic requirements
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.
Eligibility
Places are limited and open to applicants who:
- are over 18 years of age;
- are eligible to work in Northern Ireland;
- are ‘settled’ in Northern Ireland, and has been ordinarily resident in the UK for at least three years; or
- are a person who has indefinite leave to enter or remain in the UK.
- meet the course specific entry requirements. See course pages for requirements.
- meet the Ulster University general entry requirements