Master of Science in Data Science and Financial Technology

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Programme Overview

Academic Level
Academic Level

Postgraduate/Masters,

Awarded by
Awarded by

University of London, UK

Programme type
Programme type

Part-time, 2 years

Campus location
Campus location

SIM Headquarters

Application Dates
Application Dates

Apply now till 31 July 2023 (local and international applicants)

Estimated Fees (incl. GST)
Estimated Fees

(incl. GST)*

S$37,900

* All fees inclusive of current 7% GST (exclude textbooks / course materials, SIM application fee, preparatory / bridging course fee, and other fees). Fees will be subject to revised GST of 8% effective on 1 Jan 2023 and 9% on 1 Jan 2024. Refer to GST Notes under Fees Tab for more information.

Programme Outline

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.

learning-outcomes

Learning Outcomes

Upon successful completion of this programme, you will be able to: 

  1. Explain and assess a range of machine learning and financial technologies used in the big set of data and financial markets analytics. 
  2. Apply the advanced skills and specialist knowledge in the areas of machine learning and financial technologies to the design software and data analyses. 

Further Studies & Career Prospects

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.

Hear from our students
ashmita-bid
Ashmita Bid

MSc Data Science and Financial Technology, University of London

Pursuing a Future in FinTech

Read story

Why study at SIM x University of London

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SIM is the leading private education institution in Singapore.

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Excellent value with lower tuition costs in SIM.

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Goldsmiths ranked 62nd in the UK, according to the Complete University Guide 2023.

Learn more about the University of London

Intake Dates

2023 Oct intake

PROGRAMME DURATION

Oct 2023 to Sep 2025

APPLICATION PERIOD

Apply now till 31 July 2023

Early submission of eApplication is strongly recommended.

Programme Calendar (Per Semester) 

  • Study Period: Week 1 – 21 
  • Coursework 1: Week 6 or 7 
  • Coursework 2: Week 13 or 14 
  • Revision: Week 21 
  • Exams: Week 22 

 
Candidature Period 
Minimum: 2 years (depends on the University’s availability of modules) 
Maximum: 5 years 
No refund or recourse should the student fail to complete within the maximum period. 

Curriculum

Structure

  • All classes are conducted on SIM campus unless otherwise stated. 
  • Duration of each lesson is 3 hours. All lectures will be recorded and made available to students. 
  • Programme comprises the following activities.  
    • Lectures 
    • Online self-study 
    • Workshops 
    • Consultations 
  • Classes are taught by local faculty from SIM.
    View a list of lecturers’ teaching modules (PDF 531 KB)
  • Average teacher-student ratio: 1:18 
  • There must be a minimum of 25 students for the programme to commence. Students will be informed within one month prior to class commencement if the programme fails to commence due to low take up rate. 

Assessment & Attendence

  • A recommended rate of 75% attendance is to be maintained. 
  • Assessment by the University is made up of coursework and examination (for selected modules).

Module Type

Element of assessment

Element weighting

Requirement to pass the module

Core Modules

Coursework I

30% or 50%

A mark of at least 50% in both elements of assessment.

 

Coursework II / *Written examination

70% or 50%

Compulsory Modules

Coursework I

30% or 50%

A mark of at least 50% in both elements of assessment.

 

Coursework II / *Written examination

70% or 50%

Optional Modules

Coursework I

30% or 50%

A mark of at least 50% in both elements of assessment.

 

Coursework II / *Written examination

70% or 50%

Final Project

Coursework

70%

A mark of at least 50% in both elements of assessment.

 

*Written examination

30%


*The written examination is three hours in length. It comprises three sections with a mix of qualitative and quantitative questions in total. 

Requirements to Graduate

  • The award of the Master’s degree requires successful completion of all 10 modules and the final project. 
  • Grading Scheme: 
    • 70-100: Distinction 
    • 60-69: Merit 
    • 50-59: Pass 
    • 0-49: Fail . 
  • Graduates may choose to participate in the presentation ceremony on SIM campus in April or at the University of London (UK) in March. 

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) 

(Must pass all assessment elements)

Module Title

Credits

Financial Data Modelling

15

Data Programming in Python

15

Statistics and Statistical Data Mining

15

Machine Learning

15

Module Title

Credits

Big Data Analysis

15

Blockchain Programming

15

Mathematics of Financial Markets

15

(Choose any three)
 

Module Title

Credits

Artificial Intelligence

15

Data Science Research Topics

15

Data Visualisation

15

Natural Language Processing

15

Neural Networks

15

Module title

Credits

Final Project 

30


View module descriptions (PDF 531 KB)

View a sample timetable (PDF 25 KB)

*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 

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

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Admission Criteria

Accepted Entry Qualifications

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 

Entry Route 2 (only available for April intake) 
If applicants do not meet the Route 1 academic requirements, their applications may be considered based on the following  

  • A full UK second class honours degree (or its equivalent) in any subject from an institution acceptable to the University of London 
  • 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. 

 
*The subjects that would be considered as relevant are: Computing, Data Science, Computer Science, Business Computing, Games Programming, Physics, Engineering, Mathematics and statistics, 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 applications close. While completing the MOOC, students may submit an SIM application. 

English Language Requirements

GCE ‘O’ Level, min Grade C6 in English 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. 

Qualifications eligible for exemption and their corresponding RPL:

Qualification from Singapore Polytechnic

RPL from UOL courses in

  1. Specialist Diploma in Data Science (AI) 
  2. Specialist Diploma in Data Science (Big Data and Streaming Analytics) 
  3. Specialist Diploma in Data Science (Data Analytics) 
  4. Specialist Diploma in Data Science (Predictive Analytics)
  1. DSM020 Data Programming in Python* 
  2. DSM050 Data Visualisation** 


Notes: 
* Provided the candidate has successfully completed the module IT8701 Programming for Data Science 
** Provided the candidate has successfully completed one of the modules:  

  • IT8701 Programming for Data Science 
  • MS9001 Statistics for Data Science 
  • IT8302 Applied Machine Learning 

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Fees & Financial Aid

All fees inclusive of current 7% GST (exclude textbooks / course materials). Fees will be subject to revised GST of 8% effective on 1 Jan 2023 and 9% on 1 Jan 2024.

GST Notes:

  • Fees published are inclusive of current GST of 7%. The parts of fees not invoiced, not paid and where services are not rendered in year 2022 are subject to the new GST rates effective on 1 Jan 2023 and 1 Jan 2024 respectively. Refer to IRAS website here for more details.
  • For invoice generated in 2022 for services to be rendered in 2023, if payment is collected by 2022, 7% GST will apply.
  • For invoice generated in 2022, if the payment is received in 2023 and service is rendered in 2023, 8% GST will apply. Credit note against original invoice and a new invoice bearing 8% GST will be issued.
  • For invoice generated in 2022 and service is rendered in 2022, if payment is collected in 2023 (i.e. late payment), 7% still applies.


Payment to SIM can be made through SIMConnect using Credit Card (Visa or Master), PayNow, eNETS, and OCBC Interest-free Instalment Plan (min. S$500).

Programme Fees

Estimated overall fee for 10 modules and 1 final project: S$37,900* 
 

The breakdown is as follows: 
Payable to SIM 

  • SIM programme fee: S$25,894 

Payable to University of London (UOL)

  • UOL application fee: £107
  • UOL institution-supported learning fees: £5,300.40

Payable to RELC Examinations Bureau

  • UOL examination fee: S$1265

All fees inclusive of prevailing GST (exclude textbooks and valid for 2022 intake).

* These are estimated amounts as UOL and RELC fees are subject to exchange rate fluctuations, Singapore taxation and annual increase.

Additional fees payable to UOL
1. Application fee for recognition of prior learning: £61 (per module)
2. Module continuation fee (if the module is not completed in the six months study session):  £417 (per module)

Mandatory One-time Fees

All fees inclusive of prevailing GST. 

Application Fee 

Payable for each application form that is submitted. Fee is non-refundable and non-transferable. The fee will be refunded fully only if the intake does not commence. Unpaid applications will not be processed.

Local applicants:
S$96.30