(Full time) 2020 start
Advanced Computer Science (Data Analytics) MSc
Overview
Big data is becoming more and more important in fields from science to marketing, engineering, medicine and government. This programme will equip you with specialist knowledge in this exciting field and allow you to explore a range of advanced topics in computer science.
You’ll gain a foundation in topics like systems programming and algorithms, as well as the basics of machine learning and knowledge representation. You’ll also choose from optional modules focusing on topics like image analysis or text analytics, or broaden your approach with topics like cloud computing.
As one of the few schools with expertise covering text, symbolic and scientific/numerical data analysis, we can provide a breadth of expertise to equip you with a variety of skills – and you’ll work as an integral member of one of our research groups when you develop your main project. We also have close links with the Leeds Institute for Data Analytics (LIDA) which is at the forefront of big data research.
Specialist facilities
You’ll benefit from world-class facilities to support your learning, including:
- a state-of the art cloud computing lab with a 16-node cluster
- a large High Performance Computing (HPC) resource consisting of several clusters which are used for all forms of predictive modelling, data analysis and simulation
- a visualisation lab including a Powerwall, benchtop display with tracking system, WorldViz PPT optical tracking system and Intersense InertiaCube orientation tracker
- Ascension Flock of Birds tracking systems, three DOF and 6DOF Phantom force feedback devices
- Twin Immersion Corp CyberGloves
- power meters attached to cloud computing servers,
- rendering cluster and labs containing both Microsoft and Linux platforms, among others
It’s an exciting environment in which to gain a range of skills and experience cutting-edge technology.
Course content
Core modules in Semester 1 will lay the foundations of the programme by giving you an understanding of the key topics of algorithms and systems programming, as well as the basic principles of automated reasoning, machine learning and how computers can be made to represent knowledge.
From there you’ll have the chance to tailor your studies to suit your own preferences. You’ll choose from a wide range of optional modules on diverse topics such as image analysis, cloud computing, semantic technologies and developing mobile apps.
Over the summer months you’ll also work on your research project. This gives you the chance to work as an integral part of one of our active research groups, focusing on a specialist topic in computer science and selecting the appropriate research methods.
Take a look at the Advanced Computer Science (Data Analytics) module descriptions for more detail on what you will study.
Course structure
The list shown below represents typical modules/components studied and may change from time to time. Read more in our Terms and conditions.
Modules
Year 1
Compulsory modules
- Machine Learning 10 credits
- Big Data Systems 15 credits
- Data Science 15 credits
- MSc Project 60 credits
Optional modules (selection of typical options shown below)
- Web Services and Web Data 10 credits
- Distributed Systems 10 credits
- Mobile Application Development 10 credits
- Information Visualization 10 credits
- User Adaptive Intelligent Systems 10 credits
- Combinatorial Optimisation 10 credits
- Secure Computing 10 credits
- Graph Algorithms and Complexity Theory 10 credits
- Bio-Inspired Computing 15 credits
- Knowledge Representation and Reasoning 15 credits
- Parallel and Concurrent Programming 15 credits
- Data Mining and Text Analytics 15 credits
- Cloud Computing 15 credits
- Semantic Technologies and Applications 15 credits
- Image Analysis 15 credits
- Scheduling 15 credits
- Scientific Computation 15 credits
- Graph Theory: Structure and Algorithms 15 credits
Learning and teaching
Our groundbreaking research feeds directly into teaching, and you’ll have regular contact with staff who are at the forefront of their disciplines. You’ll have regular contact with them through lectures, seminars, tutorials, small group work and project meetings.
Independent study is also important to the programme, as you develop your problem-solving and research skills as well as your subject knowledge.
Assessment
You’ll be assessed using a range of techniques including case studies, technical reports, presentations, in-class tests, assignments and exams. Optional modules may also use alternative assessment methods.
Applying, fees and funding
Entry requirements
A bachelor degree with a 2:1 (hons) in computer science or a comparable subject with a substantial computing element. Relevant work experience will also be considered.
We expect you to have significant programming competence and prior experience of systems development and knowledge of data structures and algorithms.
All applicants will need to have GCSE English Language at grade C or above, or an appropriate English language qualification.
English language requirements
IELTS 6.5 overall, with no less than 6.0 in any component.. For other English qualifications, read English language equivalent qualifications.
Improve your English
International students who do not meet the English language requirements for this programme may be able to study our postgraduate pre-sessional English course, to help improve your English language level.
This pre-sessional course is designed with a progression route to your degree programme and you’ll learn academic English in the context of your subject area. To find out more, read Language for Engineering (6 weeks) and Language for Science: Engineering (10 weeks).
If you need to study for longer than 10 weeks, read more about our postgraduate pre-sessional English course.
How to apply
Application deadlines
International applicants: 13 December 2019. Due to competition for places and the volume of applications received, the deadline for international applicants has been brought forward to 13 December 2019. Applications received after this date will be marked as unsuccessful. After all applications have been considered, we will assess if there are any places still available. If so we *may* re-open applications in 2020 for competitive entry. Please check back in February 2020 for further information.
UK/EU applicants: 10 September 2020. Applicants are encouraged to apply as early as possible.
This link takes you to information on applying for taught programmes and to the University's online application system.
If you're unsure about the application process, contact the admissions team for help.
Read about visas, immigration and other information in International students. We recommend that international students apply as early as possible to ensure that they have time to apply for their visa.
Admissions policy
Faculty of Engineering and Physical Sciences Postgraduate Admissions Policy 2020
Fees
- UK/EU: £11,000 (total)
- International: £23,000 (total)
Read more about paying fees and charges.
Visit our Brexit page for the latest information on the effect of the UK's planned exit from the EU on current students and applicants to the University.
For fees information for international taught postgraduate students, read Masters fees.
Brexit
Visit our Brexit page for the latest information on the effect of the UK's exit from the EU on current students and applicants to the University.
Additional cost information
There may be additional costs related to your course or programme of study, or related to being a student at the University of Leeds. Read more about additional costs
Scholarships and financial support
If you have the talent and drive, we want you to be able to study with us, whatever your financial circumstances. There may be help for students in the form of loans and non-repayable grants from the University and from the government. Find out more at Masters funding overview.
Career opportunities
Computing is an essential component of nearly every daily activity, from the collection and processing of information in business, through to smart systems embedded in devices, image processing in medical diagnosis and the middleware that underpins distributed technologies like cloud computing and the semantic web.
This programme will give you the practical skills to enter many areas of applied computing, working as application developers, system designers and evaluators. Links between the taught modules and our research provide our students with added strengths in artificial intelligence, intelligent systems, distributed systems, and the analysis of complex data. As a result, you’ll be well-prepared for a range of careers, as well as further research at PhD level.
Graduates have found success in a wide range of careers working as business analysts, software engineers, web designers and developers, systems engineers, information analysts and app developers. Others have pursued roles in consultancy, finance, marketing and education, or set up their own businesses.
Careers support
You’ll have access to the wide range of engineering and computing careers resources held by our Employability team in our dedicated Employability Suite. You’ll have the chance to attend industry presentations book appointments with qualified careers consultants and take part in employability workshops. Our annual STEM Careers Fairs provide further opportunities to explore your career options with some of the UKs leading employers.
Projects
The professional project is one of the most satisfying elements of this course. It allows you to apply what you’ve learned to a piece of research focusing on a real-world problem, and it can be used to explore and develop your specific interests.
Recent projects for MSc Advanced Computer Science students have included:
- Text mining of e-health patient records
- Java-based visualization on ultra-high resolution displays
- Data mining of sports performance data
- Contour topology
- Efficient computation for simulating tumour growths
A proportion of projects are formally linked to industry, and can include spending time at the collaborator’s site over the summer.