University home > Unit and programme catalogues in 2022/23 > Programme catalogue > Faculty of Engineering > School of Engineering Mathematics and Technology > Data Science (MSc) (Online) > Specification
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Programme code | 4EMAT005T |
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Programme type | Postgraduate Taught Degree |
Programme director(s) |
Dave Cliff
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Faculty | Faculty of Engineering |
School/department | School of Engineering Mathematics and Technology |
Teaching institution | University of Bristol |
Awarding institution | University of Bristol |
Mode of study | Part Time |
Programme length | 2 years (part time) |
This section sets out why studying this programme is important, both in terms of inspiring you as an individual and in considering the challenges we face. It describes how this degree programme contributes to:
This MSc aims to:
Graduates from this MSc will be keenly sought after in roles such as lead data scientists or lead data engineers, capable of critically evaluating and synthesising research literature, developing and deploying scalable data-processing systems, and communicating with others in their field and in other disciplines. Top graduates from this degree will be able to pro-actively advance the development of data science and related technologies.
The learning outcome statements shown below for your programme have been developed with reference to relevant national subject benchmarks (where they exist), national qualification descriptors (see the Framework for Higher Education Qualifications) and professional body requirements.
Teaching, learning and assessment strategies are listed to show how you will be able to achieve and demonstrate the learning outcomes.
This programme provides opportunities for you to develop and demonstrate knowledge and understanding, qualities, skills and other attributes in the following areas:
Programme Intended Learning Outcomes | Learning/teaching methods and strategies |
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Methods of assessment (formative and summative) | |
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Programme Intended Learning Outcomes | Learning/teaching methods and strategies |
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Methods of assessment (formative and summative) | |
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Programme Intended Learning Outcomes | Learning/teaching methods and strategies |
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Methods of assessment (formative and summative) | |
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This section describes what is expected from you at each level of your programme. This illustrates increasing intellectual standards as you progress through the programme. These levels are mapped against the national level descriptors published by the Quality Assurance Agency.
Level M/7 - Postgraduate Certificate |
To be eligible for the award of a Postgraduate Certificate, students must successfully complete 60 credits of taught units which develop foundational understanding of the topics covered |
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Level M/7 - Postgraduate Diploma |
To be eligible for the award of a Diploma, students must successfully complete 120 credits of taught units. Further units provide opportunities for students to deepen their understanding and develop intellectual skills. By the end of this stage students are expected to have developed considerable individual self-sufficiency and autonomy in learning. |
Level M/7 - Postgraduate Masters |
To be eligible for a MSc award, 180 credits from the taught modules plus the dissertation must be successfully completed. At this stage, high-performing students will be able to carry out their own project at an industrial-quality or internationally-publishable standard. They will also be able to document and communicate their findings to peers and expert practitioners in the field. Their individual capabilities will be enhanced by teamwork and system delivery skills. |
For information on the admissions requirements for this programme please see details in the postgraduate prospectus at http://www.bristol.ac.uk/prospectus/postgraduate/ or contact the relevant academic department.
A novel aspect of the 60CP Data Science Project (TB3) is that, rather than requiring the students to submit a traditionally-structured MSc thesis, the specification for the MSc thesis on this degree will give the students the option of being able to prepare and submit a 10-page summary paper, written in a style and formatted in such a way that it would be suitable for immediate submission (without any further revision) to a major international peer-reviewed conference in data science, AI/machine learning, computer science, or in an appropriate application area. If they choose this option then additional content, longer forms of written material that would be expected in a traditional MSc thesis, can follow the 10-page paper in a series of supplementary chapters/appendices, but the examination of the thesis will be closer in spirit to reviewing a submission to a major conference than reading and assessing a long-form monograph text.
No source for further information.
Students may also take the programme part-time over 3-years. Students on the 3-year part-time programme complete the 60 credit point Data Science Project EMATM0047 unit within their third year. Students have the option to take the 20 credit point Data Science Mini-Project EMATM0050 unit in either their second or third year
Unit Name | Unit Code | Credit Points | Status | |
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Introduction to Artificial Intelligence | EMATM0044 | 10 | Mandatory | TB-2 |
Technology, Innovation, Business, and Society (TIBS) | EMATM0049 | 20 | Mandatory | TB-1 |
Introduction to Data Analytics | COMSM0089 | 10 | Mandatory | TB-2 |
Select one from: | ||||
Software Development: Programming and Algorithms | EMATM0048 | 20 | Optional | TB-1 |
Statistical Computing and Empirical Methods | EMATM0061 | 20 | Optional | TB-1 |
60 |
Please note: This specification provides a concise summary of the main features of the programme and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the learning opportunities that are provided.
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