Programme Overview

Academic Level

Postgraduate/Masters,

Awarded by

University of London, UK

Programme type

Part-time, 2 years

Campus location

SIM Campus

Application Dates

Applications are closed.

Estimated Fees
(incl. GST)*

S$39,000

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* All fees inclusive of current 9% GST (exclude textbooks / course materials, SIM application fee, preparatory / bridging course fee, and other fees). 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.

The programme is a study of a combination of long-established fields such as statistics, data mining, machine learning and databases, with modern and strongly related fields such as Big Data management and analytics, sentiment analysis and social web mining.

You will learn the computational techniques needed to efficiently analyse very large data sets under the guidance of experts in that domain. You will analyse trends in social media and make financial predictions based on the data gathered.

The programme includes:

A firm grounding in the theory of data mining, statistics and machine learning
Hands-on practical real-world applications such as social media, biomedical data and financial data with Hadoop (used by Yahoo!, Facebook, Google, Twitter, LinkedIn, IBM, Amazon, and many others), R and other specialised software
The opportunity to work with real-world software such as Apache on platforms such as Jupyter Notebook.

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:

Learn statistical skills and computational techniques to analyse very large data sets well.
Gain valuable skills in advanced data processing.

Further Studies & Career Prospects

The study could lead to a variety of potential jobs including:

Data Scientist
Big Data Analyst
Hadoop Developer
NoSQL Database Developer
Programmer
Researcher in Data Science and Data Mining

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 Sep 2025

APPLICATION PERIOD

Applications are closed.

Programme Calendar
(Per Semester)

Study Period: Week 1 – 21
Coursework 1 due: Week 6 or 7
Coursework 2 due: 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.
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 class fails to start due to low take-up.

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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.
Final Project	Coursework	70%	A mark of at least 50% in both elements of assessment.
	*Written examination	30%	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 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 can attend the April SIM campus ceremony or the March University of London ceremony (UK).

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Modules

The Master of Science in Data Science is a 180-credit programme. A student must complete:

Four core modules (60 credits total)
Two compulsory modules (30 credits total)
Four optional modules (60 credits total)
A final project (30 credits total)

(Must pass all assessment elements)

Module Title	Credits
Big Data Analysis	15
Data Programming in Python	15
Statistics and Statistical Data Mining	15
Machine Learning	15

Module Title	Credits
Data Science Research Topics	15
Data Visualisation	15

(Choose any four)

Module Title	Credits
Artificial Intelligence	15
Blockchain Programming	15
Financial Data Modelling	15
Financial Markets	15
Mathematics for Data Science	15
Natural Language Processing	15
Neural Networks	15
R for Data Science	15
Social Media and Network Science	15

Module Title	Credits
Final Project	30

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 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.

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.

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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.

Qualifications eligible for exemption and their corresponding RPL:

Qualifications from Singapore Polytechnic	RPL from UOL module 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 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 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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Master of Science in Data Science

Programme Overview

Academic Level

Awarded by

Programme type

Campus location

Application Dates

Estimated Fees
(incl. GST)*