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

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.

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.

• 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. 
• 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. 

Minimum / Maximum Period:
Minimum: 1 year
Maximum: 5 years
 

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)

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. 

Core Modules

(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

Compulsory Modules

Module Title	Credits
Big Data Analysis	15
Blockchain Programming	15
Financial Markets	15

Optional Modules

(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

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

Not Applicable

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 

Not Applicable

• 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. 

 

• 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).