| Program start date | Application deadline |
| 2025-09-01 | - |
Program Overview
The MSc Data Science program at Ulster University equips students with the skills and knowledge to analyze large data sets and employ advanced machine learning techniques. The program emphasizes interdisciplinary learning, combining theory and practical applications to prepare graduates for a successful career as Data Scientists or Business Analysts across diverse industries. Graduates are equipped with essential employability skills, including critical analysis, problem-solving, and effective communication, ensuring their success in the competitive job market.
Program Outline
MSc Data Science - Ulster University (2025/26)
Degree Overview:
Objective:
This program aims to prepare students for a successful career as Data Scientists or Business Analysts, working across diverse industries. The focus lies on developing the skills needed to analyze large data sets and employ advanced machine learning techniques to extract meaningful insights.
Description:
The curriculum emphasizes interdisciplinary learning, combining theory and practical applications to equip graduates for a rapidly changing field. Students acquire expertise in data acquisition, information extraction, aggregation, representation, and analysis using cutting-edge technologies.
Key Benefits
- Addresses industry needs for professionals with expertise in data science and analytics.
- Provides a solid foundation in the core data analytics disciplines and information processing.
- Offers practical training in state-of-the-art machine learning techniques.
- Equips graduates to work effectively across various sectors, including IT, business, security, health, intelligent transport, energy, and the creative industries.
Overall
This Master's program provides a comprehensive and in-depth understanding of Data Science, preparing individuals for a successful future in this in-demand field.
Outline
Modules
Data Science Foundations:
Introduces key data science concepts, tools, and programming techniques, including Python. Students learn how to identify and eliminate errors in large datasets, interpret descriptive statistical methods, visualize data effectively, and assess its quality.
Deep Learning and Natural Language Processing:
Explores cutting-edge techniques like Deep Learning and Natural Language Processing (NLP) in tackling complex tasks involving language, including machine translation, customer service applications, and information extraction from web searches, emails, and reports. Students gain practical experience in implementing, training, visualizing, and evaluating neural network models while learning the underlying engineering principles and theoretical advancements.
Statistical Modelling and Machine Learning:
Provides a systematic understanding of probability and statistics, delving into the process of statistical modelling for hypothesis testing and data exploration. Students gain expertise in employing and critically assessing various data mining and statistical analysis tools like Python, R, and Weka.
Research Masters Project (Optional):
Provides students with the opportunity to conduct independent research to apply their knowledge and gain practical experience. They choose between developing theoretical perspectives, answering research questions using data, or analyzing and proposing real-world solutions. This project requires critical evaluation of research methodologies and proper exposition within the chosen area of study.
Structure:
This part-time program is structured over three academic terms each year for the first year, with an optional fourth term for the third year dedicated to the project.
Assessment: Assessment methods are diverse and specific details for each module are available upon enrollment. However, the program typically utilizes the following formats:
Coursework and Assignments
- Individual or collaborative projects
- Essays
- Reports
- Presentations
- Dissertations
- Case studies
- Design and creative outputs
Teaching:
Delivery methods:
The curriculum is delivered using an engaging combination of lectures, directed tutorials, seminars, and practical sessions to ensure an interactive and comprehensive learning experience. Additionally, students receive ongoing support for project development and implementation throughout the program. They benefit from the expertise of highly qualified academic staff, including professors, readers, senior lecturers, and lecturers who are actively involved in research, ensuring that real-world applications and theoretical knowledge are seamlessly integrated to deliver an exceptional learning experience.
Teaching Staff Profile
- Over 60% of staff hold PhDs in their subject field.
- Many possess professional body recognition.
- Most hold qualifications in teaching higher education.
- Many staff are recognized as fellows by the Higher Education Academy (HEA).
- Some staff hold additional professional recognition within their respective industries and academic communities.
Unique Approach:
The program incorporates a unique focus on industry relevance through a dedicated module project. This project provides an opportunity for students to apply their acquired skills to real-world challenges in collaboration with industry partners, fostering practical experience and building valuable professional networks.
Careers:
Career Opportunities:
Graduates can pursue diverse and rewarding careers as Data Scientists, Analysts, Business Intelligence or Analytics professionals in various industries. The program equips them for positions within the following sectors and roles:
- IT: Data Analyst, Machine Learning Engineer, Data Engineer, Data Architect.
- Consulting: Data Scientist, Business Analyst, Quantitative Analyst.
- Finance and Banking: Quantitative Analyst, Risk Manager, Fraud Investigator.
- Manufacturing and Engineering: Data Analyst, Process Optimization Specialist, Machine Learning Engineer.
- Pharma and Healthcare: Biostatistician, Clinical Research Associate, Epidemiology Analyst.
- Government and Education: Policy Analyst, Data Scientist, Statistician.
- Retail and E-commerce: Business Analyst, Market Research Analyst, Customer Data Analyst.
Employability Skills
Beyond technical knowledge, the MSc in Data Science prepares individuals with essential employability skills such as:
- Critical analysis and problem-solving
- Effective communication and collaboration
- Project management and independent research skills
- Ability to adapt to evolving technological landscapes These skills equip graduates for success in a competitive job market and contribute to their long-term career growth within the data science domain.
- The Institute of Analytics (IoA): Recognized by professionals in the field.
- Ulster University Faculty of Computing, Engineering & the Built Environment: https://www.ulster.ac.uk/courses/202526
