Scientific Computing and Data Analysis (Financial Technology) draft

Program Overview

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The program combines computer science, mathematics, and financial modeling, providing a comprehensive understanding of modern financial technology. Graduates are well-positioned for careers as quantitative analysts, financial data scientists, algorithmic traders, and other roles in FinTech. The program features a research-led approach, industry connections, and state-of-the-art computing facilities.

Program Outline

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Degree Overview:

This MSc program focuses on the application of scientific computing and data analysis techniques in the financial technology (FinTech) sector. You'll explore the mathematical principles behind modern financial markets, gaining valuable skills in algorithmic trading, market making, quantitative finance, and risk management. The curriculum blends computer science, mathematics, and financial modeling, providing a comprehensive understanding of modern financial technology.

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Objectives:

• Equip graduates with the ability to write code for high-performance computing systems and process large datasets.
• Provide expertise in financial modeling, derivative pricing, and portfolio management.
• Develop critical thinking and problem-solving skills in the context of financial technology.
• Foster strong communication and collaboration skills for effective industry engagement.

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Program Description:

The program utilizes a research-led approach, allowing you to apply cutting-edge theoretical concepts in real-world financial applications. This program is ideal for individuals with a strong quantitative background in science, computer science, or mathematics who aspire to careers in FinTech, academia, or related industries.

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Outline:

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Structure:

• The program comprises a combination of lectures, practical classes, research projects, independent study, and coursework.
• You'll work with diverse high-performance computing systems and software, including GPU clusters, AI tools, and data acquisition tools.
• The program culminates in a dissertation project focusing on a chosen FinTech topic, potentially in collaboration with an industry partner.

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Course Schedule:

• Introduction to Machine Learning and Statistics: Provides knowledge and understanding of data analysis techniques.
• Introduction to Scientific and High Performance Computing: Explores the fundamentals of HPC and numerical simulation methods.
• Professional Skills: Develops collaborative coding, project management, and entrepreneurship skills.
• The Project: A significant research project on a FinTech, scientific computing, or data analysis topic.
• Financial Technology: Algorithmic Trading and Market Making in Options: Deepens your understanding of financial theory, asset valuation, and derivative pricing.
• Financial Mathematics: Introduces the mathematical theory of financial products and advanced pricing techniques.

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Additional Modules:

• Advanced Statistical and Machine Learning: Fundamentals and Unsupervised Learning
• Advanced Statistics and Machine Learning: Regression and Classification
• Data Acquisition and Image Processing
• Performance Modelling, Vectorisation and GPU Programming
• Advanced Algorithms and Discrete Systems
• Computational Linear Algebra and Continuous Systems

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Assessment:

Assessments include:

• Coursework assignments
• Presentations
• Project (33% of total mark)
The project involves conducting in-depth research and analysis, culminating in a dissertation-style report. The project topic can be chosen from within the collaborating academic departments (Mathematical Sciences, Computer Science, or others).

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Teaching:

The program is delivered by the Department of Computer Science in collaboration with the Department of Mathematical Sciences, Business School, Department of Physics, and Department of Earth Sciences.

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Teaching Methods:

• Lectures: Provide foundational knowledge and theoretical concepts.
• Practical Classes/Computer Labs: Offer hands-on experience with relevant software and technologies.
• Independent Study and Research: Encourage exploration of specific interests and project development.
• Dissertation/Project: Foster in-depth research and analysis skills.
• Group and Individual Presentations: Enhance communication and collaboration abilities.

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Faculty:

The program benefits from the expertise of leading researchers and industry professionals, ensuring a high-quality and relevant learning experience.

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Careers:

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Career Options:

Graduates of this program are well-positioned for a wide range of careers in FinTech, including:

• Quantitative Analyst
• Financial Data Scientist
• Algorithmic Trader
• Risk Analyst
• Portfolio Manager
• Financial Software Developer
• Research Scientist in FinTech

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Career Support:

The program provides career guidance and support to help graduates navigate their career paths. Resources include:

• Dedicated careers advisor
• Employability workshops
• Networking opportunities with industry professionals

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Other:

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Program Highlights:

• Strong industry connections through collaboration with leading FinTech companies.
• State-of-the-art computing facilities and software access.
• Research-informed curriculum for cutting-edge knowledge and skill development.
• Individualized project work offering the opportunity to tackle real-world challenges.

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Entry Requirements:

• UK first or upper second class honors degree (BSc) or equivalent in physics, computer science, mathematics, earth sciences, engineering, or other natural sciences with a strong quantitative element.
• Proficiency in programming (C and Python) on a graduate level.
• Background in undergraduate-level mathematics (linear algebra, calculus, integration, differential equations, probability theory).
• Minimum english language proficiency score: IELTS 6.5/ TOEFL iBT 25/ Cambridge Scale 176/ Pearson Academic 62.
• The program starts in September 2024.
• The program is one year full-time.
• The tuition fees for full-time home students are £13,500 per year.
• The tuition fees for full-time EU students are £30,900 per year.
• The tuition fees for full-time Island students are £13,500 per year.
• The tuition fees for full-time international students are £30,900 per year.

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Full Time Fees

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Tuition fees

Home students £13,500 per year EU students £30,900 per year Island students £13,500 per year International students £30,900 per year The tuition fees shown are for one complete academic year of full time study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).