MSc in Finance and Machine Learning, Queen Mary University of London

12 Months

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About this course

This MSc programme combines advanced finance and machine learning to prepare students for modern financial roles. Based at the School of Economics and Finance on Queen Mary’s Mile End campus, the course provides a thorough understanding of financial markets along with cutting-edge machine learning methods for financial applications. Students develop programming skills in languages like Python and R, enabling independent evaluation of forecasting methods and risk analysis. The curriculum covers a broad spectrum of quantitative methods used in investment decision-making, risk management, and financial analysis, integrating practical skills with theoretical foundations. The programme's structure includes core modules, electives, a research project, and opportunities for practical experience, such as working with Bloomberg platforms and participating in a student investment fund.

Why this course is highly recommended

This programme stands out due to its strong research orientation, with core modules taught by active researchers, and a practical focus on industry-relevant skills. Its strategic location near London’s financial districts offers networking opportunities with professionals, and the wide range of elective modules allows students to tailor their learning. The inclusion of professional skills modules, access to cutting-edge trading tools like Bloomberg, and the student investment fund—all contribute to hands-on experience. The course’s rankings in research output and global university rankings further support its academic excellence and industry relevance.

Specialisation

Students will focus on the intersection of finance and machine learning, gaining expertise in programming languages like Python and R, and applying advanced quantitative methods to analyze risks, returns, and financial forecasts. The course emphasizes the application of machine learning techniques such as textual analysis, large language models, and big data applications within finance, preparing students for roles in quantitative research, financial analysis, and fintech innovation.

Course fees

Application fees

1st year tuition fees

35.36L

Living cost

Degree requirements

Applicants need a minimum of a 2:1 or equivalent in any subject at undergraduate level, with a strong quantitative background in mathematics and statistics. For UK applicants, this typically means a high second-class degree, while international qualifications are assessed based on specific country standards, such as GPA, percentage scores, or specific diplomas. Additionally, proficiency in English is required, demonstrated through specified language tests.

English language test

IELTS

6.5

TOEFL

PTE

DUOLINGO

Want to learn more about the admission process, eligibility criteria, and acceptance rates for international students? Visit the Queen Mary University of London admission page for complete details.

Career prospects

Graduates from this MSc are well-prepared for careers as data analysts, quantitative researchers, fintech specialists, and risk analysts. The programme’s practical skills and industry connections facilitate employment in financial institutions, hedge funds, and technology companies working at the cutting edge of AI and finance. Queen Mary also offers extensive career support services, boosting job placement prospects for graduates.

FAQs

What programming languages will I learn?

You will develop programming skills in Python and R, among other languages, with practical modules and projects focusing on these tools.

Are there industry placements or practical opportunities?

While the programme itself emphasizes practical skills through modules like Bloomberg Market Concepts and the student investment fund, specific industry placements are not explicitly mentioned.