Master of Science in Data Science and Financial Technology (University of London)

Course Information

Course Type
Part-time
Fees (Local Students)
Estimated overall fee for 10 modules and 1 final project: S$39,000
Fees (Foreign Students)
Estimated overall fee for 10 modules and 1 final project: S$39,000
Other Fees
Application Fee: S$97.20 (local) / Application and International Student Induction Fee: S$918.00
Duration
2 years
Intake Months
Visit course webpage for intake months
Programme Grant
Visit course website for Scholarships & Bursaries
Class Schedule
Information not provided by school.
Assessment Method
Coursework and examination (for selected modules)

Description

Awarded by the University of London, UK and Developed by the Member Institution, Goldsmiths, University of London, UK.

Many experts highlight the potential for Financial Technology (FinTech) to make finance more accessible for those most in need and the FinTech sector in emerging markets and developing economies saw particularly strong growth – as much as 40% in the Middle East and North Africa. Globally, finance apps alone were downloaded more than four billion times in 2020.

This has disrupted the banking and finance industry too – from international banks to back-room start-ups – and changed the way we handle money.

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 programme combines technology from big data and analytics, mobile computing and modern financial services, to enable better decision-making for organisations.

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.

Prerequisite(s)

  • Candidates entering this programme via Entry Route 2, are required to complete an online preparatory course. For details, go to the Admission Criteria section.

*This programme is currently not accepting new applications.

Entry Requirements

Entry Route 1

Applicants must have the following:

  • A bachelor's degree (or an acceptable equivalent) in a *relevant subject which is considered at least comparable to a UK second class honours degree, from an institution acceptable to the University of London.

Entry Route 2

If applicants do not meet the above academic requirements, their applications may be considered based on the following

  • A bachelor's degree (or an acceptable equivalent) in any subject which is considered at least comparable to a UK second class honours degree, from an institution acceptable to the University of London and the successful completion of the **online preparatory course, Foundations of Data Science, prior to registration.
  • There is no entry test requirement for the MOOC course. However, there will be assessment during and at the end of the MOOC course.

*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.

**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.

English Language Requirements

Applicants must provide proof of competence in English acceptable to the University such as a minimum Grade C6 and above in the GCE 'O' Level English Language examination or its equivalent.

Exemptions

Recognition of Prior Learning (RPL) is the recognition of previously acquired learning which can be mapped against particular learning outcomes of modules within a programme. RPL may be awarded if you have previously studied a similar module in the same depth, at degree level, and you achieved good marks in the corresponding examination. A student who is awarded RPL for a specific module is considered to be exempted from the module.

The qualification on which your RPL is based must have been obtained within the five years preceding the application. Candidates must have completed all coursework and assessment for their course.

Visit course website for qualifications eligible for exemption and their corresponding RPL

Career Opportunities

Graduates will be equipped with the skills needed to go into roles across the data science and finance sector – from challenger banks and start-ups to established banks and insurance companies, marketing companies, the oil industry and the government.

Modules

Structure

  • All class are conducted on SIM campus unless otherwise stated.
  • Duration of each lesson is typically 3 hours. 
  • Programme comprises of the following activities:
    • Blended Lectures
    • Online Resources
    • Workshops
    • Consultations
  • Classes are taught by experienced lecturers from SIM.
  • There must be a minimum of 25 students for the programme to commence. Students will be informed at least one month prior if there is insufficient enrolment.

Assessment & Attendance

  • Online Tests / Coursework (each 15-credit module)
  • Capstone Project (30-credits: 25% research proposal. 75% final project)
  • Attendance requirement:
    • Local: 75%
    • International (Student Pass / Dependent Pass / Long-term Visit Pass): 90%

Modules

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 (30 credits total) 
  • Three optional modules (60 credits total) 
  • A final project (30 credits total) 

Core Modules

(Must pass all assessment elements)

  • Financial Data Modelling
  • Data Programming in Python
  • Statistics and Statistical Data Mining
  • Machine Learning

Compulsory Modules*

  • Big Data Analysis
  • Blockchain Programming
  • Financial Markets

Optional Modules*

(Choose any three)

  • Artificial Intelligence
  • Data Science Research Topics
  • Data Visualisation
  • Mathematics for Data Science
  • Natural Language Processing
  • Neural Networks
  • R for Data Science
  • Social Media and Network Science

Project

  • Final Project 

*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 elements 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 

The University will NOT allow compensation for any assessment elements within core modules and the final project. 

Locations

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