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2024/25 Taught Postgraduate Programme Catalogue

MSc Advanced Computer Science (Data Analytics)

Programme code:MSC-ACS/D-FTUCAS code:
Duration:12 Months Method of Attendance: Full Time
Programme manager:Dr Mark Walkley Contact address:m.a.walkley@leeds.ac.uk

Total credits: 180

Entry requirements:

- 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 Computer Science

Examination board through which the programme will be considered:

School of Computer Science

Relevant QAA Subject Benchmark Groups:

Computing

Programme specification:

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

[Learning Outcomes, Transferable (Key) Skills, Assessment]

Compulsory modules:

Candidates will be required to study the following compulsory modules:

COMP5122MData Science15 creditsSemester 1 (Sep to Jan)
COMP5123MCloud Computing Systems15 creditsSemester 2 (Jan to Jun)
COMP5200MMSc Project60 credits1 Jan to 30 Sep
COMP5611MMachine Learning15 creditsSemester 2 (Jan to Jun)

Optional modules:

Candidates will be required to study 75 credits from the following lists of optional modules:

COMP5125MBlockchain Technologies15 creditsSemester 2 (Jan to Jun)
COMP5450MKnowledge Representation and Reasoning15 creditsSemester 1 (Sep to Jan)
COMP5625MDeep Learning15 creditsSemester 2 (Jan to Jun)
COMP5710MAlgorithms15 creditsSemester 1 (Sep to Jan)
COMP5712MProgramming for Data Science15 creditsSemester 1 (Sep to Jan)
COMP5840MData Mining and Text Analytics15 creditsSemester 2 (Jan to Jun)
COMP5911MAdvanced Software Engineering15 creditsSemester 1 (Sep to Jan)
COMP5930MScientific Computation15 creditsSemester 1 (Sep to Jan)
COMP5940MGraph Theory: Structure and Algorithms15 creditsSemester 2 (Jan to Jun)

Last updated: 19/09/2024 16:36:14

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