We are passionate about sharing with our students the vital role they each have now and as future professionals in promoting a sustainable future for all. We believe that sustainability is not the domain of one discipline or profession. It is the responsibility of all disciplines, professions, organisations and individuals.
That is why on each of our courses within the School of Computing, Engineering and Intelligent Systems you will learn about the UN Sustainable Development Goals and the contribution you can make now, and as a graduate in Computing or Engineering.
Read the course details below to find out more.
Providing high quality professionals for the Data Science industry.
If you have previous computing experience and want to build skills to develop your career, then this is the perfect opportunity for you to begin to specialise in Data Science, a key growth area within the IT sector.
Data Science skills are typically in high demand in many industries including IT, business, security, health, intelligent transport, energy, and the creative industries. Data and analytics capabilities has 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.
To help address these challenges, the Postgraduate Certificate in Data Science will provide you with the knowledge and skills in key technologies used in data collection, curation, processing, integration, analysis, and visualisation, applied to a variety of data types. Students will be introduced to a data scientist toolkit that can be applied to build data-driven applications.
We know that choosing to study at university is a big decision, and you may not always be able to find the information you need online.
Please contact Ulster University with any queries or questions you might have about:
For any queries regarding getting help with your application, please select Admissions in the drop down below.
For queries related to course content, including modules and placements, please select Course specific information.
We look forward to hearing from you.
In this section
This specialist postgraduate course in Data Science is aimed at highly-motivated graduates with a good Honours or non-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 build skills for career development and begin to specialise in the general area of data science.
Modules
Here is a guide to the subjects studied on this course.
Courses are continually reviewed to take advantage of new teaching approaches and developments in research, industry and the professions. Please be aware that modules may change for your year of entry. The exact modules available and their order may vary depending on course updates, staff availability, timetabling and student demand.
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.
Ulster University academics are actively involved in both research and teaching and this ensures that the developments accrued through research can feed into the teaching of students. A high percentage of staff are members of the Higher Education Academy, and all staff are expected to have a Postgraduate Certificate in Higher Education Practice/University Teaching or equivalent. All Computing courses are subject to periodic Faculty Review and University Revalidation.
On successful completion of the programme, students can continue on to our MSc Data Science or our MSc Professional Software Development (Data Science). Both MSc programmes are available as full-time or part-time study options.
This is a part-time programme which begins in September and the three modules will be delivered across two semesters.
Teaching is delivered through a combination of lectures, directed tutorials, seminars and practical sessions.
The course is assessed by coursework.
The content for each course is summarised on the relevant course page, along with an overview of the modules that make up the course.
Each course is approved by the University and meets the expectations of:
As part of your course induction, you will be provided with details of the organisation and management of the course, including attendance and assessment requirements - usually in the form of a timetable. For full-time courses, the precise timetable for each semester is not confirmed until close to the start date and may be subject to some change in the early weeks as all courses settle into their planned patterns. For part-time courses which require attendance on particular days and times, an expectation of the days and periods of attendance will be included in the letter of offer. A course handbook is also made available.
Courses comprise modules for which the notional effort involved is indicated by its credit rating. Each credit point represents 10 hours of student effort. Undergraduate courses typically contain 10, 20, or 40 credit modules (more usually 20) and postgraduate courses typically 15 or 30 credit modules.
The normal study load expectation for an undergraduate full-time course of study in the standard academic year is 120 credit points. This amounts to around 36-42 hours of expected teaching and learning per week, inclusive of attendance requirements for lectures, seminars, tutorials, practical work, fieldwork or other scheduled classes, private study, and assessment. Teaching and learning activities will be in-person and/or online depending on the nature of the course. Part-time study load is the same as full-time pro-rata, with each credit point representing 10 hours of student effort.
Postgraduate Master’s courses typically comprise 180 credits, taken in three semesters when studied full-time. A Postgraduate Certificate (PGCert) comprises 60 credits and can usually be completed on a part-time basis in one year. A 120-credit Postgraduate Diploma (PGDip) can usually be completed on a part-time basis in two years.
Class contact times vary by course and type of module. Typically, for a module predominantly delivered through lectures you can expect at least 3 contact hours per week (lectures/seminars/tutorials). Laboratory classes often require a greater intensity of attendance in blocks. Some modules may combine lecture and laboratory. The precise model will depend on the course you apply for and may be subject to change from year to year for quality or enhancement reasons. Prospective students will be consulted about any significant changes.
Assessment methods vary and are defined explicitly in each module. Assessment can be a combination of examination and coursework but may also be only one of these methods. Assessment is designed to assess your achievement of the module’s stated learning outcomes. You can expect to receive timely feedback on all coursework assessments. This feedback may be issued individually and/or issued to the group and you will be encouraged to act on this feedback for your own development.
Coursework can take many forms, for example: essay, report, seminar paper, test, presentation, dissertation, design, artefacts, portfolio, journal, group work. The precise form and combination of assessment will depend on the course you apply for and the module. Details will be made available in advance through induction, the course handbook, the module specification, the assessment timetable and the assessment brief. The details are subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Normally, a module will have 4 learning outcomes, and no more than 2 items of assessment. An item of assessment can comprise more than one task. The notional workload and the equivalence across types of assessment is standardised. The module pass mark for undergraduate courses is 40%. The module pass mark for postgraduate courses is 50%.
The class of Honours awarded in Bachelor’s degrees is usually determined by calculation of an aggregate mark based on performance across the modules at Levels 5 and 6, (which correspond to the second and third year of full-time attendance).
Level 6 modules contribute 70% of the aggregate mark and Level 5 contributes 30% to the calculation of the class of the award. Classification of integrated Master’s degrees with Honours include a Level 7 component. The calculation in this case is: 50% Level 7, 30% Level 6, 20% Level 5. At least half the Level 5 modules must be studied at the University for Level 5 to be included in the calculation of the class.
All other qualifications have an overall grade determined by results in modules from the final level of study.
In Masters degrees of more than 200 credit points the final 120 points usually determine the overall grading.
Figures correct for academic year 2022-2023.
The University employs over 1,000 suitably qualified and experienced academic staff - 60% have PhDs in their subject field and many have professional body recognition.
Courses are taught by staff who are Professors (19%), Readers, Senior Lecturers (22%) or Lecturers (57%).
We require most academic staff to be qualified to teach in higher education: 82% hold either Postgraduate Certificates in Higher Education Practice or higher. Most academic and learning support staff (85%) are recognised as fellows of the Higher Education Academy (HEA) by Advance HE - the university sector professional body for teaching and learning. Many academic and technical staff hold other professional body designations related to their subject or scholarly practice.
The profiles of many academic staff can be found on the University’s departmental websites and give a detailed insight into the range of staffing and expertise. The precise staffing for a course will depend on the department(s) involved and the availability and management of staff. This is subject to change annually and is confirmed in the timetable issued at the start of the course.
Occasionally, teaching may be supplemented by suitably qualified part-time staff (usually qualified researchers) and specialist guest lecturers. In these cases, all staff are inducted, mostly through our staff development programme ‘First Steps to Teaching’. In some cases, usually for provision in one of our out-centres, Recognised University Teachers are involved, supported by the University in suitable professional development for teaching.
Figures correct for academic year 2022-2023.
Here is a guide to the subjects studied on this course.
Courses are continually reviewed to take advantage of new teaching approaches and developments in research, industry and the professions. Please be aware that modules may change for your year of entry. The exact modules available and their order may vary depending on course updates, staff availability, timetabling and student demand. Please contact the course team for the most up to date module list.
In this section
Year: 1
Status: C
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.
Year: 1
Status: C
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.
Year: 1
Status: C
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. Data mining approaches such as recommender systems, time series analysis, social network analysis will also be explored in this module.
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.
In this section
Applicants must:
(a) have gained
(i) an Honours or non-Honours degree 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 is recognised as being of an equivalent standard;
or
(ii) an equivalent standard in a Graduate Certificate or Graduate Diploma or an approved alternative qualification 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 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.
The entry requirements facilitate accreditation of prior learning.
In this section
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 Postgraduate Certificate in Data Science will pursue opportunities for industrially linked teaching material and student project work.
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.
This programme does not include a work placement.
The price of your overall programme will be determined by the number of credit points that you initiate in the relevant academic year.
For modules commenced in the academic year 2024/25, the following fees apply:
| Credit Points | NI/ROI/GB Cost | International Cost* |
|---|---|---|
| 5 | £194.45 | £474.70 |
| 10 | £388.90 | £949.40 |
| 15 | £583.35 | £1,424.10 |
| 20 | £777.80 | £1,898.80 |
| 30 | £1,166.70 | £2,848.20 |
| 60 | £2,333.40 | £5,696.40 |
| 120 | £4,666.80 | £11,392.80 |
| 180 | £7000.20 | £17,089.20 |
NB: A standard full-time PGCert is equivalent to 60 credit points per year. A standard full-time PGDip is equivalent to 120 credit points per year.
*International student access to courses is subject to meeting visa requirements. More information can be found in the Visas and Immigration section.
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.
We know that choosing to study at university is a big decision, and you may not always be able to find the information you need online.
Please contact Ulster University with any queries or questions you might have about:
For any queries regarding getting help with your application, please select Admissions in the drop down below.
For queries related to course content, including modules and placements, please select Course specific information.
We look forward to hearing from you.
