COS7004-E MSc Dissertation

Module Aims

The Dissertation for all MSc programmes is driven by the student's aspirations and preferences and draws on and enhances the professional skills and knowledge gained by the student during the taught component of the course. The student must select relevant tools and techniques acquired throughout their specific program of study (AI, Cyber Security, or Big Data) and design and develop a solution to a practical problem or challenge by using either (1) a real-world data set or (2) a generated hypothetical data set. Completing their dissertation, the student will be able to demonstrate evidence of achieving the program learning outcomes and graduate attributes and be able to o demonstrate these to future employers, including consideration of legal, social, ethical, and professional issues relevant to their topic.

Learning Outcomes

LO1: Apply practical and analytical skills developed in your programme of study.
LO2: Critically evaluate literature, technical material, and existing solutions in your program topic.
LO3: Evaluate suitable tools and techniques for design and (where appropriate, the) development of a quality solution, together with an evaluation of that solution to the subject of your project.
LO4: Demonstrate the integration of legal, social, ethical, and professional considerations and responsible computing within your dissertation report and any related artefacts (e.g. software, research findings, conclusions, recommendations).
LO5: Demonstrate research, design, and development skills within a substantial project.

Learning, Teaching and Assessment Strategy

This is the final element of the master's degree and students determine the specific applied area that they want to focus on for their dissertation based on their aspirations and preferences. Lecture sessions take place for consideration and integration of the taught material of the student's program. The lectures and seminars introduce concepts and wider context of legal, social, ethical, and professional (LSEP) practices within the discipline (e.g. analysis skills, the research process, dissertation outlines, managing projects, data protection, computer misuse, ethics etc.) The sessions define terms and implications for professional practice. A group tutorial with a case study will discuss and debate the various aspects of LSEP practice and decisions and outcomes. The tutorial will be followed by a summary plenary. To ensure important topics are developed, these initial workshops will be strengthened through discussion of these issues with your supervisor on a one-to-one basis. Students will work on an individual project with regular communication with a supervisor assisted by, if relevant, module tutors. Specification, analysis, design, development, test, maintenance, and deployment of prototype to integrate field knowledge will be addressed. The supervisors give regular formative feedback to their students.

While it has no independent weighting, a viva (max.1 hour) is held to help in confirming the overall mark for the dissertation. The viva/demo provides an opportunity for students to show systems developed during their project and demonstrate their understanding of the subject under study. Without a viva/demo there will be less evidence of the student?s work and less opportunity to fully demonstrate student understanding and attainment of the learning outcomes of the module. This may significantly affect the overall mark, with the weight attached to the viva clearly indicated on assessment marking schemes. Supplementary assessment - as original.