Master of Science in Data Science and Artificial Intelligence

University of London

Programme Overview

Academic Level
Academic Level


Awarded by
Awarded by

University of London, UK

Programme type
Programme type

Part-time, 2 years

Campus location
Campus location

Clementi Campus

Application Dates
Application Dates

Applications are closed.

Estimated Fees (incl. GST)
Estimated Fees

(incl. GST)*


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

The programme aims to give the students the fundamental knowledge and practical skills needed to design, build and apply AI systems in their chosen area of specialisation.  Data science and artificial intelligence spans across multiple research disciplines aiming to create skills needed for the digital economy.

Students will develop skills in specialist areas with clear applications in industry, including data mining, pattern recognition and machine learning.


  • 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

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

  1. Explain and assess a range of AI techniques used in data analytics.
  2. Apply knowledge and practical skills needed to design, build and apply AI systems.
  3. Develop skills with clear applications for data mining, pattern recognition & machine learning.
  4. Gain valuable skills in advanced data processing.

Further Studies & Career Prospects

There is a need for AI talents to serve the industry and to drive future research. These skills lead naturally to embarking on a variety of careers, with employers from leading technology firms, robotics, military, academia, and public research sector.

Graduates can see themselves working as software developers and engineers, programmers and data analysts; other variety of specialisms, from fraud detection to spacecraft control; and other wide range of AI-related industrial and academic posts.

Hear from our students
A Summer To Remember
A Summer To Remember

University of London (UOL) students - Andrew Lee, Khairul Hafiz, Ruchira Nikam, Vishakha Raj and Suh Jungmin - studying at the Singapore Institute of Management (SIM), share their unforgettable experiences at the LSE Summer School 2022. 60 SIM-UOL students thronged the campus in London for three weeks and more. In fact, 25 spent six weeks (two sessions) and four spent nine weeks (three sessions). The icing on the cake was LSE’s announcement that they would allow named credit transfer and half credit transfer, much to the delight of students.

Read story

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 2022.
SIM has collaborated with the Department of Computing at Goldsmiths since 1993.
Learn more about the University of London

Intake Dates

2023 Oct intake


Oct 2023 to Sep 2025


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: 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.



  • 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 388 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



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


*Written examination


*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.


The Master of Science in Data Science and Artificial Intelligence 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)

(Must pass all assessment elements)

Module Title


Artificial Intelligence


Data Programming in Python


Statistics and Statistical Data Mining


Machine Learning


Module Title


Big Data Analysis


Data Science Research Topics


Neural Networks


(Choose any three)

Module Title


Blockchain Programming


Data Visualisation


Financial Data Modelling


Mathematics of Financial Markets


Natural Language Processing


Module Title


Final Project 


View module descriptions (PDF 91 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 elementelements 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. 


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 (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 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, 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

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.


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:

Qualifications from Singapore Polytechnic

RPL from UOL module 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**

* 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


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$39,000*

The breakdown is as follows:

Payable to SIM
SIM programme fee: S$27,216

Payable to University of London (UOL)

  • UOL application fee: £107
  • UOL institution-supported learning fees: £7,572

Payable to RELC Examinations Bureau

  • UOL examination fee: S$1,366.20

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