Program Overview
The MSc Financial Data Science program at the University of Birmingham equips students with advanced statistical techniques and machine learning algorithms tailored for the financial services industry. Through core modules, optional modules, and practical projects, students gain a comprehensive understanding of financial data science and develop skills to analyze data and make informed decisions in dynamic market environments. The program prepares graduates for a wide range of careers in finance, data analysis, and financial technology.
Program Outline
Degree Overview:
The MSc Financial Data Science program at the University of Birmingham is a one-year, full-time postgraduate program designed to equip students with the skills and knowledge needed to succeed in the financial industry. It combines expertise in finance, mathematics, statistics, and data science, providing a comprehensive and interdisciplinary approach to financial education.
Objectives:
The program aims to:
- Master the tools needed to navigate the complexities of modern finance.
- Develop advanced statistical techniques and machine learning algorithms tailored for the financial services industry.
- Gain practical experience in harnessing data to drive informed financial decisions.
- Equip students with the foundational knowledge and practical skills needed to analyze financial data effectively and make informed decisions in a dynamic market environment.
- Prepare students for a wide range of careers in finance, data analysis, and financial technology.
Description:
The program empowers students with advanced statistical techniques and machine learning algorithms tailored specifically for the financial services industry. Through hands-on projects and real-world applications, students gain practical experience in harnessing data to drive informed financial decisions. The core modules cover essential topics including statistical inference, deep learning, time series analysis, and algorithmic trading. These modules equip students with the foundational knowledge and practical skills needed to analyze financial data effectively and make informed decisions in a dynamic market environment. In addition to the core modules, students have the opportunity to tailor their learning experience by choosing from a range of optional modules. These modules explore advanced topics such as financial mathematics, statistical modelling, machine learning, computational statistics, and stochastic processes, allowing students to specialize in areas of particular interest. Throughout the program, students engage in hands-on learning experiences, including practical projects and case studies that apply theoretical concepts to real-world financial datasets. Students also benefit from guest lectures and seminars delivered by industry experts, providing valuable insights into current trends and practices in the financial industry.
Outline:
The course consists of 180 credits, two-thirds from taught modules (core modules are compulsory, choose several optional modules) and one-third from your research project.
Core Modules:
- Algorithmic and High Frequency Trading - 10 credits
- Deep Learning 1 - 10 credits
- Foundations of Statistical Inference - 20 credits
- Time Series and Prediction - 10 credits
- Financial Data Science Project - 60 credits
Optional Modules:
Choose 70 credits. Example optional modules are listed below:
- Advanced Mathematical Finance - 20 credits
- Bayesian Inference and Computation - 20 credits
- Computational Statistics - 10 credits
- Data Visualisation - 10 credits
- Deep Learning 2 - 10 credits
- Financial Mathematics - 20 credits
- Interest Rate and Credit Risk Modelling - 10 credits
- Largescale Optimization for Machine Learning - 10 credits
- Mathematical Finance - 20 credits
- Mathematical Securitisation - 10 credits
- Quantitative Funds Management - 10 credits
- Statistical Machine Learning - 20 credits
- Statistical Modelling - 20 credits
Teaching:
The program emphasizes hands-on learning experiences, including practical projects and case studies that apply theoretical concepts to real-world financial datasets. Students also benefit from guest lectures and seminars delivered by industry experts, providing valuable insights into current trends and practices in the financial industry.
Careers:
Graduates of the MSc Financial Data Science program are equipped with a versatile skill set that opens doors to a wide range of career opportunities in the financial industry and beyond. Some potential career paths include:
- Quantitative Analyst
- Data Scientist
- Financial Engineer
- Risk Analyst
- Investment Analyst
- Financial Technology (FinTech) Specialist
- Consultant
Other:
The program fosters a collaborative learning environment. Through group projects, seminars, and networking events, students engage with peers and industry professionals, building valuable connections and honing their teamwork and communication skills.
- Annual tuition fee for 2024/25 £10,530 - UK students £27,270 - International students
- Are you an international applicant? All international applicants to this course will be required to pay a non-refundable deposit of £2,000 on receipt of an offer, to secure their place. Find out more about the deposit >>.
- Postgraduate Loans (PGL) for Masters students UK and EU students (with settled or pre-settled status) looking to pursue a Masters programme in the UK can apply for a non-means-tested loan from the British government via the Student Loans Company (SLC). The loan will be paid directly to you, into a UK bank account. It is intended to provide a contribution towards the costs of Masters study and whether the loan is used towards fees, maintenance or other costs is at your own discretion. Scholarships We offer a range of
- Scholarships for 2024 entry With a scholarship pot worth over £2 million, we are committed to alleviating financial barriers to support you in taking your next steps. Each scholarship has its own specific deadlines and eligibility criteria.
