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MSc Financial Mathematics with Data Science

  • DeadlineStudy Details: MSc 1 year full-time

Course Description

Standout to employers in the financial sector with a strong foundation in quantitative, mathematical and computational skills relevant to that industry.

Finance is a dynamic industry, with innovation in quantitative methods driving fast-growing areas such as FinTech. Advances in machine learning and increased availability of data are allowing organisations to make better decisions and improve their products and services.

Implementing these advances requires a new generation of graduates with a range of skills in quantitative, mathematical and data science fields.

This course will reinforce your mathematical skills across a wide range of topics and equip you with quantitative skills sought after by employers in the financial industry. You'll gain a broad education in classical and contemporary mathematical finance and data science methods relevant to modern financial institutions. You’ll develop a practical and theoretical understanding of machine learning and other data science tools, and the software skills needed to successfully implement them.

Who is this course for?

This course is designed for graduates in highly numerate disciplines who are interested in a career in the financial industry and would like to develop their knowledge of this area. The course provides training in programming, machine learning, data science and financial mathematics.

Course highlights

  • Benefit from a course developed with input from leading industry experts and ensure you’re up to date with the latest advances in modern finance.
  • Stand out when applying for jobs by gaining a strong foundation in both financial mathematics and data science.
  • Open up a wide range of careers within the financial industry.
  • Undertake an intensive research project in collaboration with academics, tackling a problem of importance to the financial industry.
  • Be part of our supportive postgraduate community.
  • Live and study in a beautiful world heritage city.

Summer research project

For your summer research project you will be supported to undertake an in-depth investigation into the application of mathematics or data science to a problem of importance in the financial industry.

Entry Requirements

You should have a first or strong second-class undergraduate degree or international equivalent.

To apply for this course, your undergraduate degree should be in a subject that incorporates a substantial mathematical element such as mathematics, statistics, computer science, physics, chemistry, engineering or economics. Computer programming experience would also be advantageous.

We will also consider other subjects, for example geography or biology, which may meet the criteria depending on their specific mathematical and computing content.

We may make an offer based on a lower grade if you can provide evidence of your suitability for the degree.

If your first language is not English but within the last 2 years you completed your degree in the UK you may be exempt from our English language requirements.

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Fees

For fees and funding options, please visit website to find out more

Student Destinations

On graduation, you'll have a broad range of skills and knowledge relevant to a career in traditional and modern financial sectors. From banking, insurance, investment and risk management, to leading areas of the modern financial industry such as FinTech, employers are seeking specialists with financial mathematics and data science skills. Our dedicated careers team offers individual guidance and can help you decide between employment and further study.

Recent graduates from the department are in positions in a wide range of financial sectors including: foreign exchange trading, credit risk, fund management, insurance and actuarial consulting in companies ranging from start-up FinTech companies to multi-national, big-name banks and insurers.

Module Details

Year 1

Semester 1

Compulsory units

  • Financial models in discrete and continuous time
    10 credits
  • Foundations and applications of machine learning
    10 credits
  • Programming for data science
    10 credits
  • Risk, randomness and optimisation
    5 credits
  • Statistics for data science
    5 credits

Semester 2

Compulsory units

  • Advanced techniques for finance
    10 credits
  • Bayesian data analysis
    5 credits
  • Financial models in discrete and continuous time
    Continued
  • Foundations and applications of machine learning
    Continued
  • Research project preparation
    5 credits

Summer

Compulsory units

  • Dissertation
    30 credits

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