MSc Finance and Machine Learning

QMUL-Mile End London

 

INTAKE: September

Program Overview

The MSc Finance and Machine Learning program at Queen Mary University of London aims to equip students with the necessary knowledge and skills to pursue careers in finance using machine learning techniques. The program is designed to provide students with an understanding of finance, financial data analysis, and programming, as well as machine learning algorithms and applications. Students will gain a comprehensive understanding of financial theory and learn to apply data analysis and machine learning techniques to analyze financial data and make informed investment decisions.

The program is taught by faculty from the School of Economics and Finance, the School of Mathematical Sciences, and the School of Electronic Engineering and Computer Science. Students will have the opportunity to engage in cutting-edge research and work with leading academics and practitioners in the field.The program consists of core modules covering financial theory, econometrics, data analysis, and machine learning, as well as optional modules allowing students to specialize in areas such as quantitative trading, risk management, or financial engineering.

Students will also undertake a research project, where they will apply their skills to a real-world problem in finance.The MSc Finance and Machine Learning program provides a unique combination of finance and machine learning knowledge, preparing students for exciting careers in the finance industry.

Pollster Education

Location

QMUL-Mile End London

Pollster Education

Score

IELTS: 6.5

Pollster Education

Tuition Fee

£ 28950

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. 

  1. 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.
  2. 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.
  3. 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.
  4. 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.

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Graduates of this program may also pursue further academic studies, such as a PhD in finance or machine learning.


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