This course will equip you with the advanced algorithm and programming skills needed to work in data analytics ('Big data') and the freedom to specialise on particular kinds of data. This includes: text analysis and data mining (online data, health informatics and business), images (medical applications) and scientific computing and visualisation (engineering and science). It also provides you with exposure to other cutting-edge technologies (cloud and mobile applications).
Leeds is uniquely placed to deliver this breadth of experience, with world-leading research across many of the major applications areas, in which data analysis is crucial to success. In fact, we are one of the few schools with expertise covering text, symbolic and scientific/numerical data analysis.
The strong interdisciplinary element to our research means that we are exceptionally well placed to provide our students with problems and data from a wide variety of other disciplines.
Science, business, engineering, medicine and government all rely on extracting insight, answers or commercial value from increasingly large and complex data, which includes databases, online sources, medical imaging and computer simulation.
You will undertake a substantial research project during the summer months, where you will apply your knowledge and skills to tackle a significant problem, which will prepare you for a graduate career in the IT industry or for further study.
Course duration: 12 months (full-time)
Start date: September
Course fees (full-time): UK/EU: £10,000; International: £18,500
Scholarships: Scholarships worth up to £3,000 available.
The table below shows the modules that you will study if you commence your studies in September 2015. This information is taken from the University Programme Catalogue, which is a tool designed for current students to select modules.
If you are looking to start your studies in September 2016, it will provide you with an example of what you will study.
All of our MSc courses operate on a credit-based modular system. A standard module is typically worth 15 credits and the research project/dissertation is worth 60 credits. You are required to study 180 credits in total.
|Knowledge Representation and Machine Learning||15|
(study at least 15 credits)
|Data Mining and Text Analytics||15|
(study up to 60 credits)
|Advanced Distributed Systems
|Mobile Application Development||15|
|Semantic Technologies and Applications||15|
|Graph Theory: Structure and Algorithms||15|
This is an indicative list and actual content may vary as we regularly review the content of our courses in light of new experiences and developments in the field.
Depending on the focus taken in the course, destinations include companies, trading houses and banks (e.g. data mining, trend analysis and forecasting), medicine and health (e.g. delivering medical image analysis in clinical or system suppliers), major engineering companies (scientific computing and visualization),
and research laboratories (public or commercial).
Every year our Careers Centre undertakes a destination of leavers survey with students six months after graduation. A selection of these destinations, from the last five years, can be found on our employability page.
A UK upper second class honours degree (2.1), or equivalent, in computing or a related discipline with a significant computing component. You will be expected to have programming competence, some prior systems development experience and knowledge of data structures and algorithms. Relevant work experience will be taken into consideration.
We also welcome and accept students with a wide range of international equivalent qualifications.
English language requirement: GCSE English Language grade C (or above) or an equivalent recognised English Language qualification e.g. IELTS: 6.5 with not less than 6.0 in listening, reading, speaking and writing.
Our Language Centre provide a range of English for Academic Purposes Pre-sessional courses, which are designed to help international students develop their language and academic study skills.
If you require any further information please contact our admissions team,
e: firstname.lastname@example.org, t: +44 (0)113 343 5440.