Programme Overview

Academic Level

Postgraduate/Masters,

Awarded by

University of London, UK

Programme type

Part-time, 1.5 years

Campus location

SIM Campus

Application Dates

Applications are closed.

Estimated Fees
(incl. GST)*

S$26,190

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* All fees inclusive of current 9% GST (exclude textbooks / course materials, SIM application fee, preparatory / bridging course. Refer to GST Notes under Fees Tab for more information.

Programme Outline

Awarded by 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 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.

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Learning Outcomes

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

Explain and assess a range of machine learning and financial technologies used in the big set of data and financial markets analytics.
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

This is a stackable programme. Graduates may further their studies in the MSc in Data Science and Financial Technology at SIM or in the institutions around the world (subject to their admission criteria) upon completion of this programme.

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.

Why study at SIM x University of London

SIM is the leading private education institution in Singapore.

Excellent value with lower tuition costs in SIM.

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

SIM has collaborated with the Department of Computing at Goldsmiths since 1993.

Learn more about the University of London

Intake Dates

2023 Oct intake

PROGRAMME DURATION

Oct 2023 to Mar 2025

APPLICATION PERIOD

Applications are closed.

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: 1.5 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.
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
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.

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Assessment & Attendance

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

*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 postgraduate diploma requires successful completion of all 8 modules.
Grading Scheme:
- 70-100: Distinction
- 60-69: Merit
- 50-59: Pass
- 0-49: Fail
Graduates can attend the April SIM campus ceremony or the March University of London ceremony (UK).

Modules

The Postgraduate Diploma in Data Science and Financial Technology is a 120-credit programme. A student must complete:

Four core modules (60 credits total)
Three compulsory modules (45 credits total)
One optional modules (15 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
Financial Markets	15

(Choose any one)

Module Title	Credits
Artificial Intelligence	15
Data Science Research Topics	15
Data Visualisation	15
Mathematics for Data Science	15
Natural Language Processing	15
Neural Networks	15
R for Data Science	15
Social Media and Network Science	15

View module descriptions (PDF 106 KB)
View a sample timetable (PDF 25 KB)

*Rules for Compensation
The University will allow compensation for an assessment element within optional 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.

Admission Criteria

Accepted Entry Qualifications

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

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English Language Requirements

Applicants must provide proof of competence in English acceptable to the University of London such as a minimum Grade C6 and above in the GCE 'O' Level English Language examinations 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. before the application intake is closed. While completing the MOOC course, student may submit an SIM application.

Qualifications eligible for exemption and their corresponding RPL:

Qualification from Singapore Polytechnic	RPL from UOL courses in
Specialist Diploma in Data Science (AI) Specialist Diploma in Data Science (Big Data and Streaming Analytics) Specialist Diploma in Data Science (Data Analytics) Specialist Diploma in Data Science (Predictive Analytics)	DSM020 Data Programming in Python* 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 9% GST (exclude textbooks / course materials).

GST Notes:

Fees published are inclusive of current GST of 9%. The parts of fees not invoiced, not paid and where services are not rendered in year 2023 are subject to the new GST rates effective on 1 Jan 2024. Refer to IRAS website here for more details.
For invoice generated in 2023 for services to be rendered in 2024, if payment is collected by 2023, 8% GST will apply.
For invoice generated in 2023, if the payment is received in 2024 and service is rendered in 2024, 9% GST will apply. Credit note against original invoice and a new invoice bearing 9% GST will be issued.
For invoice generated in 2023 and service is rendered in 2023, if payment is collected in 2024 (i.e. late payment), 8% still applies.

View all payment modes

Programme Fees 

Estimated overall fees for 8 modules: S$26,190*

The breakdown is as follows:

Payable to SIM

SIM programme fee: S$18,144

Payable to University of London (UOL)

UOL application fee: £107
UOL institution-supported learning fees (4 modules): £5,048

Payable to RELC Examinations Bureau

UOL examination fee: S$993.60

All fees inclusive of prevailing GST (exclude textbooks and valid for 2023 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)

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Mandatory 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$98.10

Scholarships & Bursaries

SIM GE Bursary

The SIM GE Bursary aims to provide financial assistance to needy students studying at SIM GE. This award is given based on our eligibility criteria, as well as the assessed level of neediness of the applicants.

Learn more

SIM GE Bursary

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Postgraduate Diploma in Data Science and Financial Technology

Programme Overview

Academic Level

Awarded by

Programme type

Campus location

Application Dates

Estimated Fees
(incl. GST)*