Department of Computer Science,
Faculty of Engineering
This course is designed for students with a numerate background (for example a first degree in mathematics, economics, engineering, physics or chemistry) as well as graduates already working in industry.
The course focuses on managing vast amounts of information and transforming it into actionable knowledge. It teaches the key skills that are required to carry out practical analysis of the types of data sets that need to be interpreted in the modern world. These include large data sets as well as structured and unstructured data. The course makes use of techniques developed within a range of disciplines, including computer science, artificial intelligence, mathematics and statistics.
- Scalable Machine Learning
- Text Processing
- Machine Learning and Adaptive Intelligence
- Natural Language Processing
- Industrial Team Project
- Individual Data Analytics Dissertation
- Professional Issues
- Statistical Data Science in R
- Computer Security and Forensics
- Parallel Computing with Graphical Processing Units (GPUs)
- Modelling and Simulation of Natural Systems
- Network Performance Analysis
- Web Technologies
The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption. We are no longer offering unrestricted module choice. If your course included unrestricted modules, your department will provide a list of modules from their own and other subject areas that you can choose from.
We use lectures, tutorials and group work.
Assessment is by formal examinations, coursework assignments and a dissertation.
1 year full-time
Within 3 months of starting the course I secured a graduate placement as a Technology Analyst at Barclays for after my masters, which I would not have got if I had not studied this course!
Minimum 2:1 honours degree in a numerate discipline (computer science, mathematics, economics, engineering, physics, chemistry).
Overall IELTS score of 6.5 with a minimum of 6.0 in each component, or equivalent.
If you have any questions about entry requirements, please contact the department.
Fees and funding
+44 114 222 1800
Any supervisors and research areas listed are indicative and may change before the start of the course.
Recognition of professional qualifications: from 1 January 2021, in order to have any UK professional qualifications recognised for work in an EU country across a number of regulated and other professions you need to apply to the host country for recognition. Read information from the UK government and the EU Regulated Professions Database.