Financial Technology (FinTech) is disrupting the banking and finance industry. The way we handle money is changing and the whole industry is taking note – from international banks to back room start-ups.
This innovative programme will provide graduates with in-demand quantitative and analytical skills necessary to embark on a successful career in FinTech or in the financial services sector.
The Data Science and Financial Technology programme combines the technology from big data and analytics, mobile computing and modern financial services. Goldsmiths’ Computing Department believes that the critical factor in the study is learning by doing and experimenting. The required modules and an applied project will allow the student to gain a strong foundation of knowledge, as well as practical experience and the opportunity to tailor learning to meet individual career ambitions.
These skills lead naturally to embarking on a variety of careers with employers from financial sector, including financial planning, insurance, marketing, and investment banking.
The Master of Science in Data Science and Financial Technology is a 180 credit programme. A student must complete:
- Four core modules (60 credits total)
- Three compulsory modules (45 credits total)
- Three optional modules (45 credits total)
- A final project (30 credits total)
CORE (must pass all assessment elements)
- Financial Data Modelling 15
- Data Programming in Python 15
- Mathematics and Statistics for Data Science 15
- Machine Learning 15
- COMPULSORY (subject to the *rules for compensation)
- Big Data Analysis 15
- Blockchain Programming 15
- Mathematics of Financial Markets
OPTIONAL (choose any three and subject to the *rules for compensation)
- Artificial Intelligence 15
- Data Science Research Topics 15
- Data Visualisation 15
- Natural Language Processing 15
- Neural Networks 15
- R for Data Science 15
PROJECT (must pass all assessment elements)
Final Project in Data Science and Financial Technology 30
Rules for Compensation
The university will allow compensation for an assessment element within optional and compulsory modules if:
- the mark awarded for one of the assessment element is between 45%-49%; AND
- the mark for the other assessment elements is sufficient to produce an overall combined weighted pass mark for the module
Note: the university will NOT allow compensation for any assessment element within core modules and the final project.
Entry Route 1
Applicants must have the following:
- A full UK second class honours degree (or its equivalent) in a *relevant subject from an institution acceptable to the University of London; AND
- GCE ‘O’ Level, min Grade C6 in English or its equivalent
The subjects that would be considered as relevant are: Computing, Data Science, Computer Science, Business Computing, Games Programming, Physics, Engineering, Mathematics and statistics, Finance, Marketing and Finance.
Entry Route 2
If applicants do not meet the above academic requirements, their applications may be considered based on the following
- A full UK second class honours degree (or its equivalent) in a any subject from an institution acceptable to the University of London; AND
- GCE ‘O’ Level, min Grade C6 in English or its equivalent; AND
- *Complete and pass an online preparatory course, MOOC via Coursera platform. There is no entry test requirement for the MOOC course. However, there will be assessment during and at the end of the MOOC course
*Students should sign up with Coursera at least 3 months before the intended intake. Do aim to have the results at least one month before the application intake is closed. While completing the MOOC course, student may submit an SIM application.