Data Science

Program Overview

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Data Science - MSc

2025/26 Full-time Postgraduate course

Award:

Master of Science

Faculty:

Faculty of Computing, Engineering and the Built Environment

School:

School of Computing, Engineering and Intelligent Systems

Campus:

Derry~Londonderry campus

Start dates:

• September 2025
• January 2026

Overview

Providing high quality professionals for the Data Science industry.

Summary

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 data are 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.

Modules

• Data Science Foundations: 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: 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: 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: 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: 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: 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: 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.

Attendance

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.

Teaching, Learning and Assessment

Teaching is delivered through a combination of lectures, directed tutorials, seminars and practical sessions. Support is also provided for project preparation and implementation.

The course is assessed by coursework.

Standard entry conditions

We recognise a range of qualifications for admission to our courses. In addition to the specific entry conditions for this course you must also meet the University’s General Entrance Requirements.

Entry Requirements

Applicants must:

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

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.

Careers & opportunities

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.

Professional Recognition

Accreditations reflect the excellence of our teaching, research, and knowledge exchange and ensure our programmes realise the highest expectations. By studying at Ulster University you’ll gain insight and be at the forefront of current industry practices, while our many accredited degree programmes open doors to the world’s top professional organisations, making you more attractive to future employers and giving you a competitive edge in the job market.

BCS, the Chartered Institute for IT

Accredited by BCS, the Chartered Institute for IT for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional.

BCS, the Chartered Institute for IT

Accredited by BCS, the Chartered Institute for IT on behalf of the Engineering Council for the purposes of partially meeting the academic requirement for registration as a Chartered Engineer.

The Institute of Analytics (IoA)

Accredited by The Institute of Analytics (IoA) which is the Professional Body for Analytics and Data Science Professionals worldwide.

Fees and funding

Tuition Fee Loans Available

Students domiciled in Northern Ireland, Republic of Ireland and UK students can apply to Student Finance NI for a Tuition Fee loan of up to £6,500 (subject to eligibility).

Northern Ireland, Republic of Ireland and EU Settlement Status Fees

£7,240.00

International Fees

£17,810.00

Fees Notice - January Start

Important Notice: Fees information for programmes with a January 2026 start date

Ulster University has two main intakes for Academic Year 25/26 – 1. September 2025 and 2. January 2026.
Many of our programmes which start in January will continue into Academic Year 26/27 and consequently any modules undertaken in Academic Year 26/27 will be charged at our 26/27 prices.

If your study continues into future academic years your fees are subject to an annual increase. Please take this into consideration when you estimate your total fees for a degree.

International Students

• International Student fees for programmes commencing in January 2026

Students from Northern Ireland, Republic of Ireland and Great Britain

For those who are starting a programme in January which continues into Academic Year 26/27, 26/27 fees are currently not available. To help with budget planning please refer to the 25/26 fees. These are subject to increase.

• Visit our Fees pages for full details of fees

Additional mandatory costs are highlighted where they are known in advance. There are other costs associated with university study.

Fees are subject to annual increase. Correct at the time of publishing. Terms and conditions apply.

Scholarships, awards and prizes

Sponsored prize for the best student performance in taught modules.

View Available Scholarships

See if you can access financial or other forms of support, including mentorship to excel in your studies.

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Additional mandatory costs

None

It is important to remember that costs associated with accommodation, travel (including car parking charges) and normal living will need to be covered in addition to tuition fees.

Where a course has additional mandatory expenses (in addition to tuition fees) we make every effort to highlight them above. We aim to provide students with the learning materials needed to support their studies. Our libraries are a valuable resource with an extensive collection of books and journals, as well as first-class facilities and IT equipment. Computer suites and free Wi-Fi are also available on each of the campuses.

There are additional fees for graduation ceremonies, examination resits and library fines.

Students choosing a period of paid work placement or study abroad as a part of their course should be aware that there may be additional travel and living costs, as well as tuition fees.

See the tuition fees on our student guide for most up to date costs.