2020/21 Taught Postgraduate Programme Catalogue
MSc Advanced Computer Science (Data Analytics)
|Programme code:||MSC-ACS/D-FT||UCAS code:|
|Duration:||12 Months||Method of Attendance:||Full Time|
|Programme manager:||Dr Mark Walkley||Contact address:||firstname.lastname@example.org|
Total credits: 180
A minimum UK Upper Second Class Honours (2.1) degree or equivalent in computing or a scientific subject with significant computing component;
- A pass at GCSE level English Language (grade C or above);
- International students must have an English language qualification at a suitable level: IELTS 6.5 or equivalent.
School/Unit responsible for the parenting of students and programme:
School of Computing
Examination board through which the programme will be considered:
School of Computing
Relevant QAA Subject Benchmark Groups:
On completion of the programme students should be a able to demonstrate:
- a systematic understanding of the theory and practice of designing and implementing computer systems
- proficiency in the technical and programming skills required to design and implement computer systems;
- a thorough knowledge and skills base in a number of advanced topics within the domain of computer science;
- an in-depth knowledge of the essential principles and practices of designing and using computer systems to perform data analysis tasks;
- the ability to apply these principles and practices to tackle a significant data analysis problem within the main project;
- an in-depth understanding of an area of specialisation, gained during the main project;
- be confident in applying the research methodology adopted for the main project on new problems;
- be prepared for further study either in the context of professional development or through further engagement in higher education.
The programme will:
- provide the opportunity to study all components of the data analysis pipeline including machine learning techniques used in data mining; computational modelling of data; techniques for visualizing high dimensional complex data and usability issues of data analysis systems.
- explore methods used for different types of data in particular text and image data.
- be rooted in established research strengths of the School and will offer the opportunity for students to work as integral members of our research groups during their main project.
- prepare graduates for graduate careers in the IT industry and other contexts or for further study either in the context of continuing professional development or through further engagement in higher education.
Year1 - View timetable
Candidates will be required to study the following compulsory modules:
|COMP5111M||Big Data Systems||15 credits||Semester 2 (Jan to Jun)|
|COMP5122M||Data Science||15 credits||Semester 2 (Jan to Jun), 1 Jun to 30 Sep|
|COMP5200M||MSc Project||60 credits||1 Jan to 30 Sep, 1 Apr to 31 Dec|
|COMP5611M||Machine Learning||15 credits||1 Sep to 31 Jan (adv yr), Semester 1 (Sep to Jan)|
Candidates will be required to study 75 credits from the following lists of optional modules:
|COMP5450M||Knowledge Representation and Reasoning||15 credits||1 Sep to 31 Jan (adv yr), Semester 1 (Sep to Jan)|
|COMP5623M||Artificial Intelligence||15 credits||Semester 2 (Jan to Jun)|
|COMP5712M||Programming for Data Science||15 credits||Semester 1 (Sep to Jan), 1 Jun to 30 Sep, 1 Jan to 31 May, 1 Sep to 31 Jan (adv yr)|
|COMP5840M||Data Mining and Text Analytics||15 credits||Semester 2 (Jan to Jun)|
|COMP5850M||Cloud Computing||15 credits||Semester 2 (Jan to Jun)|
|COMP5911M||Advanced Software Engineering||15 credits||Semester 1 (Sep to Jan), 1 Sep to 31 Jan (adv yr)|
|COMP5930M||Scientific Computation||15 credits||Semester 1 (Sep to Jan), 1 Sep to 31 Jan (adv yr)|
Last updated: 28/09/2020 09:01:48
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