Taught course

Scientific Computing and Data Analysis (Computer Vision and Robotics)

Durham University · Department of Computer Science

Entry requirements

A UK first or upper second class honours degree (BSc) or equivalent

  • In Physics or a subject with basic physics courses OR
  • In Computer Science OR
  • In Mathematics OR
  • In Earth Sciences OR
  • In Engineering OR
  • In any natural sciences with a strong quantitative

We strongly encourage students to sign up for a specialisation area for which they already have a strong background or affinity. At the moment, the course targets primarily Physics, Earth Sciences and Mathematics (finances) students. If you do not have a degree from these subjects, we strongly recommend you to contact the University beforehand to clarify whether you bring along the right background. Please note that standard business degrees are not sufficient, as they lack the required level of mathematical education.

Additional requirements

Programming knowledge on an graduate level in both C and Python is required.

Months of entry


Course content

Developments in fields such robotics, physics, engineering, earth sciences or finance are increasingly driven by experts in computational techniques. Those with the skills to write code for the most powerful computers in the world and to process the biggest data sets in the world can truly make a difference.

Our suite of Masters in Scientific Computing and Data Analysis (MISCADA) offers an application-focused course to deliver these skills with three interwoven strands:

  • Computer Science underpinnings of scientific computing (algorithms, data structures, implementation techniques, and computer tool usage)
  • Mathematical aspects of data analysis and the simulation and analysis of mathematical models
  • Implementation and application of fundamental techniques in an area of specialisation (as well as Computer Vision and Robotics we offer options in Astrophysics, Earth and Environmental Sciences, or Financial Technology)

The MISCADA specialist qualification in Computer Vision and Robotics is designed to equip you with the background knowledge and skills to address some of the biggest research questions in computer vision and robotics, such as how we can develop future mobility solutions which combine autonomy with safety and reliability. The course explores areas such as computer vision, machine learning, robotic motion and planning, as well as reinforcement learning. You can find out more here.

There’s great synergy between the modules and you will be given plenty of opportunities to put your learning into practice from the start of the course. Our research-led approach allows you to take some of the newest theoretical ideas and directly translate them into working codes in their respective application areas. If you have an undergraduate degree in a science subject with a strong quantitative element, including computer science and mathematics and want to work at the highest level in computer vision and robotics, either in academia or in industry, then this could be the course you’re looking for.

Course Structure

Core modules:

Introduction to Machine Learning and Statistics provides knowledge and understanding of the fundamental ideas and techniques in the application of data analysis, statistics and machine learning to scientific data.

Introduction to Scientific and High Performance Computing provides knowledge and understanding of paradigms, fundamental ideas and trends in High Performance Computing (HPC) and methods of numerical simulation.

Professional Skills provides C refresher training with an outlook into large-scale code usage. You will also develop wider professional skills in areas such as entrepreneurship, intellectual property and build the skill you will need to communicate novel ideas in science, and reflect on ethical issues around data and research.

The Project is a substantive piece of research into an unfamiliar area of robotics, scientific computing or data analysis, or a related area in cooperation with an industry partner. The project will develop your research, analysis and report-writing skills.

Computer Vision explores contemporary concepts, approaches and algorithms in computer vision and examines how current research is applied in the industry. Examples of themes include stereo vision, object tracking, real-time processing approaches, scene reconstruction from multiple image, object detection, and applications of computer vision for autonomous navigation

Robotics – Planning and Motion develops your knowledge of key concepts, approaches and algorithms in robotics, and how current research is applied in the industry. Examples of themes include robot classification, position and orientation, typical actuators/sensors and feedback control, simultaneous localisation and mapping (SLAM), and path planning and obstacle avoidance.

Deep Learning for Computer Vision and Robotics explores key concepts, approaches and algorithms for the use of deep machine learning and its application within industry. Examples of themes include scene reconstruction and understanding from multiple images, video or active sensing; simultaneous localisation and mapping (SLAM) from varying sensor inputs; visual odometry from varying sensor inputs; and robotic guidance and control.

Plus optional modules which may include:

  • Advanced Statistical and Machine Learning: Foundations and Unsupervised Learning
  • Advanced Statistics and Machine Learning: Regression and Classification
  • Data Acquisition and Image Processing
  • Performance Engineering and Advanced Algorithms
  • Continuous and Discrete Systems

Information for international students

If you are an international student who does not meet the requirements for direct entry to this degree, you may be eligible to take a pre-Masters pathway programme at the Durham University International Study Centre.

Fees and funding

UK students
International students

For further information see the course listing.

Qualification, course duration and attendance options

  • MSc
    full time
    12 months
    • Campus-based learningis available for this qualification

Course contact details

Recruitment and Admissions