Typical Course Length
Data Science is a rapidly growing interdisciplinary area, with applications in business, government, public health, and the sciences. Its applications range from identifying customers’ buying patterns and monitoring machinery to tracking the spread of a disease and logging and improving an individual's health. There is therefore a huge unfilled demand for graduates with skills in ‘Big Data’.
Why study MSc Data Science at Aberystwyth University?
- Aberystwyth University is a top 50 university for research power and intensity – REF 2014
- Opportunity to study in Departments with links to many major companies and a strong research focus
- Opportunity for graduates of any discipline to significantly enhance their employability
- Opportunity to apply the knowledge from this course to other areas, including your initial undergraduate discipline
- 100% of the Department of Computer Science’s research was deemed either world-leading or internationally excellent in terms of research impact – REF 2014
- 98% of the Department of Computer Science’s research, and 100% of the Department of Mathematical Sciences’ research was of an internationally recognised standard or higher – REF 2014
- Aberystwyth Department of Computer Science graduates exceed the national subject area average for employability
- Our taught masters degrees are designed to meet the needs both of students intending a career in research, and those who want to accelerate an industrial career
Almost all of the Department of Computer Science Lecturers and Teaching Fellows are qualified to PhD level, and those who are not have considerable research or industrial experience. All new Lecturers and Teaching Fellows are required to obtain the PGCTHE, and hence are Senior Fellows or Fellows of the Higher Education Academy. The department also employs a number of part time demonstrators and tutors and some student demonstrators, who are selected from our undergraduate and postgraduate students. Research fellows and research assistants (mostly PhD qualified) may also be involved in delivering occasional teaching when it is appropriate.
All lecturers in the Department of Mathematics are qualified to PhD level and are research active. The majority have a postgraduate teaching qualification and new staff are required to complete the PGCTHE. The department also employs a number of part time tutors, with extensive teaching experience, and some student demonstrators, who are selected from our undergraduate and postgraduate students.
Please note: The modules listed below are those currently intended for delivery during the next academic year and may be subject to change. They are included here to give an indication of how the course is structured.
|Module Name||Module Code||Credit Value|
|Modelling, Managing and Securing Data||CSM3120||20|
|Programming for Scientists||CSM0120||20|
|Statistical Concepts, Methods and Tools||MAM5120||20|
|Applied Data Mining||CSM6720||20|
|Machine Learning for Intelligent Systems||CSM6420||20|
|Statistical Techniques for Computational Scientists||MAM5220||20|
|Module Name||Module Code||Credit Value|
The range of careers to which our graduates advance is vast and continuously expanding. Examples of pathways our previous graduates have taken include working for/ as:
- Investment banks
- Product Managers
- setting up their own companies
- Developing careers in research
There are many opportunities for Data Scientists in the jobs market, and whatever your intention post-MSc, you will be offered support with your career planning.
Throughout this course, students will develop skills, qualities, and expertise that will make them extremely marketable to employers. On this course you will:
- Develop specialized technical skills in the areas of data handling, data management, data analytics and data mining, relational modelling, cryptography, and system security
- Develop subject-specific expertise, including an awareness of the legal, social, ethical and professional issues involved in handling data, and knowledge of statistical techniques and methods for large data sets
- Develop study and research skills
- Enhance your problem solving and analytical abilities
- Enhance your communication skills through a diverse mixture of learning and assessment methods
University Careers Service
Nationwide employers in the industry of Computer Science visit the university’s Careers Fairs. The university’s Careers Service also offers a wide variety of specialist services.
Teaching & Learning
How will I be taught?
This course can be taken as a full-time one year course and can also be taken part-time. When taken full time, the course is divided over three semesters.
During the first two semesters (September to May) students complete 120 taught credits. The third semester (June to September) is given over to the MSc project and dissertation (60 credits). Contact time for this course is approximately 12 hours a week in the first two semesters.
During semester three, you will arrange your level of contact time with your assigned supervisor. The taught part of the course is delivered through lectures, student seminars and practical exercises.
What will I learn?
In the first two semesters you will undertake a number of core modules. These module include:
- Machine Learning for Intelligent Systems
- Applied Data Mining (offered by the Department of Computer Science)
- Statistical Techniques for Large Data Sets (offered by the Department of Mathematics). This provides excellent opportunities to benefit from expertise in both departments.
- In the third semester, you will complete a supervised master’s project and dissertation, worth 60 credits.
How will I be assessed?
Assessment is a mix of written assignments, programming assignments, practical portfolios, practical examinations and written examinations. The successful submission of your research dissertation in semester three leads to the award of an MSc.