Department of Computer Science | MSc Scientific Computing and Data Analysis (Financial Technology)
12 monthsProgramme duration
34,500 GBPTuition Fee/year
SepStarting Month
Programme overview
Main Subject
Computer Science and Information Systems
Degree
MSc
Study Level
Masters
Study Mode
On Campus
Developments in fields such as finance, physics and engineering are increasingly driven by experts in computational techniques. The financial services sector has always been at the forefront of data analytics, and those with the skills to write code for the most powerful computers in the world and to process the biggest data sets can give a company a competitive edge.
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 Financial Technology, we offer options in Astrophysics, Engineering, AI Platforms, Computer Vision and Robotics, or Environmental and Geographic Information Systems)
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 financial technology, either in academia or in industry, then this could be the course you’re looking for.
Programme overview
Main Subject
Computer Science and Information Systems
Degree
MSc
Study Level
Masters
Study Mode
On Campus
Developments in fields such as finance, physics and engineering are increasingly driven by experts in computational techniques. The financial services sector has always been at the forefront of data analytics, and those with the skills to write code for the most powerful computers in the world and to process the biggest data sets can give a company a competitive edge.
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 Financial Technology, we offer options in Astrophysics, Engineering, AI Platforms, Computer Vision and Robotics, or Environmental and Geographic Information Systems)
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 financial technology, either in academia or in industry, then this could be the course you’re looking for.
Admission Requirements
- In Mathematics OR
- In Computer Science OR
- In Earth Sciences OR
- In Engineering OR
- In Physics OR
- In any natural sciences with a strong quantitative element.
Additional requirements
- Applicants must demonstrate strong programming skills in at least one compiled language, preferably C or C++, although Rust, Java, C#, Fortran, or Pascal are also acceptable. Proficiency in Python may suffice if the applicant has a strong background in their chosen specialisation. Those lacking experience in C or C++ are advised to enrol in our pre-sessional course.
- Additionally, we require knowledge of undergraduate-level mathematics, covering linear algebra, statistics and calculus
Tuition fees
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