MSc Data Science - 2022

Educational Aims Of Programme

• A solid foundation for a career or further study applied data science.
• Coverage of the core areas of data science.
• A solid grounding in the theoretical underpinnings of contemporary developments in data science.
• A solid grounding in practical data science skills.

Programme Outcomes and Learning, Teaching and Assessment Strategies

Knowledge and Understanding

• The theoretical principles of the core techniques of applied data science
• Software tools and frameworks for data science
• The application of data science to practical problems

Skills & Other Attributes

• The ability to apply the knowledge and understanding noted above to the analysis of a given data science problem.
• The ability to specify, design and construct solutions to data science problems, using appropriate tools, and to document all stages of this process.
• The ability to evaluate a range of methodological approaches in relation to a given data science problem.
• The ability to work independently on a substantial data science project and as an effective team member.
• Entrepreneurial skills, including project management
• Numeracy, in both understanding and presenting cases involving quantitative or similar formal, symbolic dimensions.
• Interpersonal skills

Transferable Skills

Footnotes

• Optional Modules: Not all options may be available in any particular year. Some option combinations are only available if the timetable permits. As students may have to make preliminary option choices before timetables are available, changes may be needed later if there are clashes. In selecting options, students need to pay attention to pre- and co-requisites.
• Programme Approval: Students' individual programmes of study must be approved by the Programme Director (as the nominee of the Head of School).
• Project: Project topics must be approved by the Programme Director as being appropriate to the Aims and Learning Outcomes of the programme.