BSc (Econ)(Hons) Economics, Finance and Management
Spanning three years, with options to extend to four or five years to include a year ab...
MSc Finance and Machine Learning
QMUL-Mile End London
INTAKE: September
Program Overview
The MSc in Finance and Machine Learning provides a robust foundation in finance, complemented by cutting-edge training in machine learning methodologies applicable to financial contexts. Students develop proficiency in programming languages such as Python and R, enabling them to implement and assess forecasting models and quantitative analyses pertinent to investment decisions.
Curriculum: The program structure includes six compulsory modules covering topics like Corporate Finance, Big Data Applications for Finance, Asset Pricing, Trading and Portfolio Construction, Introduction to Machine Learning, Quantitative Methods in R, and Large Language Models and Textual Analysis in Finance. Students can tailor their learning experience by selecting from a range of elective modules, including Financial Derivatives, International Finance, Behavioural Finance, and Fintech. The program culminates in a dissertation or a research project, allowing students to explore specific areas of interest in depth.
Research Focus: Emphasizing the integration of theoretical knowledge with practical application, the program encourages students to engage in research that addresses real-world financial challenges. The dissertation or research project component enables students to conduct independent studies, often involving empirical analysis, to contribute to the field of finance and machine learning.
Industry Engagement: Queen Mary's School of Economics and Finance maintains strong connections with the financial sector, incorporating insights from industry professionals into the academic environment. Elective modules are often taught by finance-industry practitioners, providing students with exposure to current industry practices and trends. Additionally, the school offers professional development modules on programming languages and trading platforms, enhancing students' practical skills and employability.
Global Perspective: Located in London, a major global financial hub, Queen Mary University offers students a diverse and international learning environment. The MSc program attracts students from around the world, fostering a multicultural academic community. This global perspective enriches the learning experience, preparing graduates to navigate and excel in international financial markets.
Location
QMUL-Mile End London
Score
IELTS: 6.5
Tuition Fee
£ 33500
Postgraduate Entry Requirements:
- Applicants should have successfully completed a bachelor's degree or its equivalent from a recognized institution with a minimum overall score of 60% or equivalent.
- English language proficiency is required, and applicants must provide evidence of their English language skills through an approved language test.
- IELTS: A minimum overall score of 6.5 with no individual component below 6.0.
- TOEFL: A minimum overall score of 92, with at least with at least 17 in Listening, 18 in Reading, 20 in Speaking, and 21 in Writing.
- PTE Academic: A minimum overall score of 71 with 65 in Writing, and 59 in Reading, Listening and Speaking..
- Some postgraduate programs may have specific subject prerequisites or additional requirements.
Students must provide:
- academic marksheets & transcripts
- letters of recommendation
- a personal statement - SOP
- passport
- other supporting documents as required by the university.
Work experience: Some postgraduate courses may require relevant work experience in the field.
It is important to note that meeting the minimum entry requirements does not guarantee admission, as the university considers factors such as availability of places and competition for the program. Additionally, some courses may have higher entry requirements or additional selection criteria, such as interviews or portfolio submissions.
Queen Mary University of London offers a range of scholarships and bursaries to its students.
Queen Mary Principal's Scholars Program: A scholarship program for undergraduate students who have an offer of admission from Queen Mary University of London and have achieved exceptional academic grades. The scholarship covers full tuition fees for three years of study, as well as a £2,000 annual stipend for living expenses.
Queen Mary International Excellence Scholarships: A scholarship program for international undergraduate and postgraduate students who have an offer of admission from Queen Mary University of London and have demonstrated academic excellence. The scholarship covers full tuition fees for one year of study.
Queen Mary Law Scholarships: A scholarship program for undergraduate and postgraduate law students who have an offer of admission from Queen Mary University of London and have demonstrated academic excellence. The scholarship covers full or partial tuition fees, depending on the level of academic achievement.
Queen Mary Engineering and Materials Science Scholarships: A scholarship program for undergraduate and postgraduate students studying engineering or materials science who have an offer of admission from Queen Mary University of London and have demonstrated academic excellence. The scholarship covers full or partial tuition fees, depending on the level of academic achievement.
The MSc Finance and Machine Learning program at Queen Mary University of London aims to equip students with the skills and knowledge to work at the intersection of finance and data science. Graduates of this program have a wide range of career prospects in both the finance and technology sectors.
Quantitative Analyst: These professionals use statistical and mathematical models to identify and exploit trading opportunities in financial markets. They work with large datasets and use machine learning algorithms to develop predictive models.
Data Scientist: Data scientists analyze complex datasets to identify trends and patterns that can be used to inform business decisions. They use machine learning algorithms to develop predictive models and statistical models to identify correlations in data.
Risk Manager: Risk managers analyze and evaluate risks associated with financial investments and transactions. They use statistical models and machine learning algorithms to identify and mitigate risks in investment portfolios and financial transactions.
Financial Analyst: Financial analysts evaluate financial data to inform investment decisions. They use statistical models and machine learning algorithms to develop financial models and identify trends in financial markets.
Technology Consultant: Technology consultants provide advice and guidance to companies on how to use technology to improve their business operations. They work with companies to identify opportunities to implement machine learning and data analytics tools to improve financial performance.
