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QS Rank:

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120

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Queen Mary University of London

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London

United Kingdom

The MSc in Finance and Machine Learning at Queen Mary University of London is a premier program designed to equip students with cutting-edge knowledge and skills at the intersection of finance and technology. This unique program is structured to meet the growing demand for finance professionals who are adept in data analysis and machine learning techniques. As industries become increasingly reliant on data-driven decision-making, the integration of finance concepts with machine learning applications has become essential. This program aims to prepare students not only to understand traditional finance but also to leverage advanced computational techniques to derive insights from complex financial data.

Throughout the 12-month course, students will engage with a comprehensive curriculum comprising core finance courses alongside specialized modules in machine learning. Key subjects include Investment Analysis, Machine Learning for Finance, Financial Econometrics, and Risk Management. The program emphasizes both theoretical foundations and practical applications, ensuring that graduates are well-versed in financial theories while also proficient in employing machine learning algorithms to solve real-world financial problems. By incorporating group projects, case studies, and practical sessions, the curriculum fosters collaboration and critical thinking, allowing students to develop essential skills needed in today’s job market.

The faculty at Queen Mary University of London comprises leading researchers and industry practitioners who bring a wealth of experience and expertise to the classroom. Their innovative teaching methodologies combine lectures, interactive workshops, and mentorship opportunities that encourage students to engage actively with the course material. Faculty members are committed to providing personalized support, with many serving as research supervisors, helping students dive deeper into their specific areas of interest within finance and machine learning. This close interaction between faculty and students cultivates a collaborative learning environment that promotes academic excellence.

Research opportunities abound for students in the MSc in Finance and Machine Learning. Queen Mary University of London is equipped with state-of-the-art facilities, including advanced finance and data analytics labs, where students can explore their research interests. The university encourages students to participate in ongoing research projects, providing access to valuable resources such as financial databases and analytical software. This hands-on experience not only enhances students’ understanding of complex concepts but also prepares them for careers that demand a high level of analytical and research proficiency.

Additionally, the program boasts strong industry connections, offering students numerous internship opportunities with leading financial institutions and tech companies. These internships are designed not only to provide practical experience but also to facilitate networking with industry professionals, greatly enhancing career prospects upon graduation. As students complete their internships, they gain insights into the financial sector's real-world dynamics while building a robust professional network that can be advantageous in their future careers.

Graduates of the MSc in Finance and Machine Learning can expect to pursue a variety of career pathways, including roles such as Financial Analyst, Data Scientist, Quantitative Analyst, and Risk Manager, among others. The program’s focus on both finance and machine learning ensures that students possess the dual expertise that employers are seeking. Alumni have successfully secured positions in top-tier firms across sectors such as finance, consulting, and technology, showcasing the program's effectiveness in preparing students for the competitive job market.

The unique features of this program distinguish it from others. The integration of machine learning with finance is a cutting-edge approach that is increasingly relevant in today’s data-driven environment. Moreover, the diverse international student body enriches the overall learning experience, allowing for the exchange of different perspectives and ideas. Students will also benefit from career workshops, guest lectures from industry leaders, and access to job fairs that connect them with potential employers.

Why Study MSc in Finance and Machine Learning at Queen Mary University of London?

  • Diverse and International Community: The university is home to a rich tapestry of cultures, fostering an inclusive environment that enhances the educational experience.
  • Expert Faculty: Learn from distinguished faculty members who are leaders in their fields, offering mentorship and guidance to enhance your academic journey.
  • State-of-the-Art Facilities: Access to high-end labs and resource centers enables students to conduct groundbreaking research and practical work.
  • Strong Industry Links: Benefit from valuable internship placements and networking opportunities with industry leaders to kickstart your career.
  • High Employability Rate: Graduates are highly sought after, with many securing positions in prestigious organizations shortly after graduation.
  • Comprehensive Curriculum: A well-rounded academic framework that includes advanced finance topics and practical machine learning applications.

To gain admission into this esteemed program, prospective students should possess a relevant undergraduate degree in finance, mathematics, statistics, or a related field. Strong quantitative skills and familiarity with programming languages such as Python or R will also be beneficial. Additionally, proficiency in English is required, with accepted language test scores including an IELTS minimum score of 6.5, a PTE score of at least 62, or a TOEFL score of 92. These prerequisites ensure that students are prepared to handle the rigorous academic challenges of the program.

In summary, the MSc in Finance and Machine Learning at Queen Mary University of London represents an exceptional opportunity for students to immerse themselves in a unique educational experience that is both academically enriching and practically relevant. With a focus on cutting-edge research, industry connections, and a robust curriculum, graduates are well-positioned to thrive in the evolving landscape of finance and technology.

intake

Duration

12 Months

Ranking

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#110

US World and News Report

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#117

The World University Rankings

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#120

QS World University Rankings

Class Profile

Diversity

North America:

2%

South America:

1%

Asia:

10%

EU:

12%

Others:

3%

Africa:

3%

UK:

68%

Eligibility Criteria

English Proficiency Tests

  • IELTS

    6.5

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  • PTE

    62

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  • TOEFL

    92

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FAQs

  • Admissions Requirements:

    To gain admission into the MSc in Finance and Machine Learning at Queen Mary University of London, prospective students should meet the following criteria:

    • Possess a relevant undergraduate degree in finance, mathematics, statistics, or a related field.
    • Demonstrate strong quantitative skills.
    • Familiarity with programming languages such as Python or R is beneficial.
    • Proficiency in English is required, with accepted language test scores including:
      • IELTS minimum score of 6.5
      • PTE score of at least 62
      • TOEFL score of 92

    These prerequisites ensure that students are prepared to handle the rigorous academic challenges of the program.

  • Program Structure:

    The MSc in Finance and Machine Learning is a 12-month course that includes a comprehensive curriculum designed to provide students with both theoretical foundations and practical applications. The program structure comprises:

    • Core finance courses.
    • Specialized modules in machine learning, including:
      • Investment Analysis
      • Machine Learning for Finance
      • Financial Econometrics
      • Risk Management

    Students will engage in group projects, case studies, and practical sessions to foster collaboration and critical thinking, equipping them with essential skills needed in today’s job market.

  • Teaching Methods:

    The faculty at Queen Mary University of London employs innovative teaching methodologies that include:

    • Lectures
    • Interactive workshops
    • Mentorship opportunities

    These methods encourage active engagement with the course material. Faculty members are dedicated to providing personalized support, often serving as research supervisors to help students explore their specific interests within finance and machine learning, fostering a collaborative learning environment.

  • Career Prospects:

    Graduates of the MSc in Finance and Machine Learning can anticipate pursuing a variety of career pathways, including:

    • Financial Analyst
    • Data Scientist
    • Quantitative Analyst
    • Risk Manager

    The program’s dual focus on finance and machine learning equips students with the expertise that employers highly seek. Many alumni have successfully secured positions in top-tier firms across sectors such as finance, consulting, and technology, demonstrating the program's effectiveness in preparing students for a competitive job market.

  • Unique Aspects:

    The MSc in Finance and Machine Learning program offers several unique features that distinguish it from other programs, including:

    • Integration of machine learning with finance, providing a cutting-edge approach relevant in today’s data-driven environment.
    • A diverse international student body that enriches the learning experience through the exchange of different perspectives and ideas.
    • Career workshops, guest lectures from industry leaders, and access to job fairs that connect students with potential employers.
  • Research Opportunities:

    Research opportunities are abundant for students in the MSc in Finance and Machine Learning program. Key features include:

    • Access to state-of-the-art facilities, including advanced finance and data analytics labs.
    • Encouragement to participate in ongoing research projects.
    • Access to valuable resources such as financial databases and analytical software.

    This hands-on experience enhances students’ understanding of complex concepts and prepares them for careers demanding high analytical and research proficiency.

  • Industry Connections:

    The MSc in Finance and Machine Learning program boasts strong industry connections, which include:

    • Numerous internship opportunities with leading financial institutions and tech companies.
    • Practical experience designed to facilitate networking with industry professionals, enhancing career prospects upon graduation.

    These internships offer insights into the real-world dynamics of the financial sector while helping students build a robust professional network, advantageous for their future careers.

  • Program Highlights:

    Key highlights of the MSc in Finance and Machine Learning program include:

    • Diverse and international community that fosters an inclusive educational environment.
    • Expert faculty who provide mentorship and guidance to enhance the academic journey.
    • State-of-the-art facilities that support groundbreaking research and practical work.
    • Strong industry links offering valuable internship placements and networking opportunities.
    • High employability rate with many graduates securing positions in prestigious organizations shortly after graduation.
    • A comprehensive curriculum that integrates advanced finance topics with practical machine learning applications.
  • Program Focus:

    The MSc in Finance and Machine Learning program at Queen Mary University of London focuses on equipping students with cutting-edge knowledge and skills at the intersection of finance and technology. The primary objectives include:

    • Preparing students to understand traditional finance concepts.
    • Leveraging advanced computational techniques to derive insights from complex financial data.

    This focus addresses the growing demand for finance professionals who are adept in data analysis and machine learning techniques, ensuring graduates are well-prepared for the evolving landscape of finance and technology.

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