Computational Finance MSc

1 Year On Campus Masters Program

Royal Holloway University of London

Program Overview

The MSc in Computational Finance at Royal Holloway equips you with cutting-edge quantitative finance and computational modelling skills, blending deep theoretical knowledge with real-world applications in pricing, risk assessment, and financial analytics. It is ideal for students who want to launch a career at the intersection of finance, machine learning, and computational data science, whether in the City, FinTech, or global financial centres.

Curriculum Structure

Year 1
In your first year, you will build a robust foundation in computational finance through core modules such as Data Analysis, where you learn machine learning techniques like clustering and regression, and Foundations of Finance, offering essential quantitative tools used in analysing markets. Investment and Portfolio Management deepens your understanding of portfolio construction and risk-return optimisation, while the Individual Project lets you apply these skills to a substantial research or software project.

Optional/Follow-on Learning Opportunities
Alongside the core content, you can tailor your studies with electives such as Machine Learning (developing practical ML systems), Methods of Computational Finance (exploring pricing and hedging models), and Large-scale Data Storage and Processing (hands-on experience with big data frameworks). These options allow you to align your learning with your career ambitions in finance, analytics, or technology.

Focus areas:

Quantitative finance, computational modelling, machine learning applications in finance, financial analytics, risk assessment, programming in MATLAB/R, financial market theory

Learning outcomes:

Apply advanced computational methods to real-world financial problems; critically evaluate finance and machine learning techniques; develop and implement programmes using industry-standard tools; undertake independent research in computational finance

Professional alignment (accreditation):

While this MSc does not require external accreditation like a professional body title (e.g., CFA), it is designed in partnership between the Department of Computer Science and the Department of Economics to meet industry demand for quantitative and computational skills

Reputation (employability rankings):

Royal Holloway is a respected UK research university with strong employability outcomes; graduates of this programme have secured roles at organisations including Amazon, Google, JP Morgan, UBS, and EY, benefitting from industry links and networking opportunities thanks to proximity to major financial and tech hubs

Experiential Learning (Research, Projects, Internships etc.)

At Royal Holloway’s MSc in Computational Finance, you won’t just study theory — you’ll gain hands-on experience with the tools and environments that finance professionals use every day. You will work directly with industry-standard software like MATLAB and R, handling real datasets and building analytical and predictive models. The programme also connects you with employers through seminars and career support, helping bridge the gap between study and work, whether you choose the one-year track or the optional Year in Industry. Weekly seminar series expose you to current practices and provide opportunities to discuss real problems with professionals, strengthening your practical skills and confidence.

Here’s what your experiential learning might involve:

Work on projects using MATLAB, R, and SQL-based data management systems, building real analytical solutions and financial models as part of coursework and your individual project.
Participate in weekly seminars and workshops led by industry professionals, exploring current technologies and practicing problem-solving and communication skills.
Complete small group projects and assessed work that reflect real team-based tasks common in computational finance roles, giving experience with collaboration and project delivery.
For those on the Year in Industry route, spend up to a full year in a paid professional placement with companies such as Centrica, Disney, Shell, UBS and others, gaining workplace experience that feeds directly into your degree and enhances employability.
Benefit from support from the Careers and Employability Service, including one-to-one coaching, employer events, and help in preparing for interviews and placement applications.

Progression & Future Opportunities

Graduates of the MSc in Computational Finance at Royal Holloway are well-prepared to enter a wide range of roles in finance and technology. Many go on to work as quantitative analysts, risk managers, data scientists, or financial software developers, combining strong analytical skills with practical computational expertise. The programme equips you with both the technical and professional skills needed to thrive in the fast-moving world of finance:

The university’s Careers and Employability Service offers one-to-one guidance, employer networking events, CV and interview preparation, and help securing placements in leading financial institutions.
Graduates from Royal Holloway’s computing and finance programmes have high employability rates, with many securing roles in top-tier companies and competitive salaries reflecting the demand for computational finance expertise.
The programme maintains strong university–industry partnerships with organisations such as UBS, Shell, Disney, and Centrica, providing opportunities for projects, seminars, and Year in Industry placements.
The skills and knowledge gained from this MSc provide long-term value for professional accreditation and recognition in quantitative finance, analytics, and related areas.
Graduation outcomes typically include roles in quantitative finance, algorithmic trading, risk management, data analytics, and positions within FinTech companies, providing a clear path into high-demand careers.

Further Academic Progression:
After completing this MSc, students can continue their studies through research-focused pathways such as MPhil or PhD programmes in Computational Finance, Financial Engineering, or Data Science. The strong foundation in quantitative methods, programming, and financial theory also prepares students to pursue professional certifications, such as CFA or FRM, further enhancing career prospects and expertise in the financial industry.