Data Science - MSc - Video
Providing high quality professionals for the Data Science industry.Take a look
In this section
Providing high quality professionals for the Data Science industry.
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 is collected, hence there is a need for skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation. These type of skills are typically in high demand in IT business, security and health sectors, intelligent transport, energy efficiency 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.
Sign up for course updates
Sign up to receive regular updates, news and information on courses, events and developments at Ulster University.
We’ll not share your information and you can unsubscribe at any time.
About this course
In this section
This intensive two-years+ 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.
This is a two-years+ part-time programme, with the taught modules delivered across the first two semesters during each of the first two years. Three taught modules are normally completed in each of the first two years. Each lecture will be hosted at the Magee campus with a live feed to the Jordanstown campus. A lab facilitator will be available each week to assist students on a one-to-one basis. The project is normally completed in the third year, but may be 'fast-tracked' in year 2 (if desired).
- September 2019
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
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.
Machine Learning and Data Modelling
This module covers Machine Learning both conceptually and practically. Students will be introduced to a variety of unsupervised and supervised Machine Learning techniques. Once the core concepts have been introduced they will be given practical experience of their use, application and evaluation through laboratory exercises and a project. The students will develop an in-depth understanding of the potential and scope of applying and evaluating the different forms of Machine Learning. This will allow them to develop a range of applications from simple practical implementations to large scale implementations.
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. Material covered will be contextualised by providing examples of the latest research within the area. Students will also be introduced to programming with Python / R. They will learn the basics of syntax, and how to configure their development environment for implementation and testing of algorithms related to data science.
This module aims to contextualise the role of Business Intelligence (BI) and why we need BI systems. A particular focus will be on how to turn already stored data into valuable information and why this is important. 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. Big Data technologies do offer BI although however, they are open to certain ethical and consent issues along with risks. These will be analysed, reviewed and evaluated.
Big Data and Infrastructure
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, semantic store 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. Students will be taught, practically and theoretically, about the components of Hadoop, workflows, functional programming concepts, use of MapReduce, Spark, Pig, Hive and Sqoop.
Statistical Modelling and Data Mining
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. Online tools, such as Blackboard will be used to facilitate blended learning approach. 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.
Masters Project (Research)
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 and appropriate analytical models. This may involve the collection of primary or secondary data and the qualitative or quantitative analysis of the data. This will typically be followed by a structured analysis of needs for a realistic application or actual organisation and identification and application of tools/techniques required to deliver a well formed solution. Through the project the student will develop capabilities to analyse cases studies related to data science and/or business intelligence, and create improvement plans and recommendations for their implementation based upon the tools/technologies experienced during the programme of study.
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.
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
(a) have gained
(i) a second class honours degree or better 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
(b) 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.
Ulster recognises a number of other English language tests and comparable IELTS equivalent scores.
Teaching and learning 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.
Exemptions and transferability
The entry requirements facilitate accreditation of prior learning.
Careers & opportunities
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 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.
Work placement / study abroad
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 University Teaching or equivalent. All Computing courses are subject to periodic Faculty Review and University Revalidation.
Fees and funding
In this section
Fees (total cost)
Important notice - fees information
Fees illustrated are based on 19/20 entry and are subject to an annual increase. Correct at the time of publishing. Terms and conditions apply. Additional mandatory costs are highlighted where they are known in advance. There are other costs associated with university study.
Visit our Fees pages for full details of fees
- Northern Ireland & EU:
- £14,060.00 Scholarships available
Scholarships, awards and prizes
Industry sponsored prize for the best overall student performance.
Additional mandatory costs
Tuition fees and costs associated with accommodation, travel (including car parking charges), and normal living are a part of university life.
Where a course has additional mandatory expenses we make every effort to highlight them. These may include residential visits, field trips, materials (e.g. art, design, engineering) inoculations, security checks, computer equipment, uniforms, professional memberships etc.
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 wifi is also available on each of the campuses.
There will be some additional costs to being a student which cannot be itemised and these will be different for each student. You may choose to purchase your own textbooks and course materials or prefer your own computer and software. Printing and binding may also be required. There are additional fees for graduation ceremonies, examination resits and library fines. Additional costs vary from course to course.
Students choosing a period of paid work placement or study abroad as part of their course should be aware that there may be additional travel and living costs as well as tuition fees.
Please contact the course team for more information.
Dr Bryan Gardiner
School of Computing, Engineering and Intelligent Systems