Master Of Technology

Master Of Technology


ISS offers Master of Technology (MTech) degrees in Software Engineering (SE), Knowledge Engineering (KE) andEnterprise Business Analytics (EBAC). Each programme leads to the award of a Master's degree by the National University of Singapore.

Each programme extends over a minimum period of two and one-half years, and a maximum of five years of part-time study. The programmes are also available on a full-time basis over a minimum period of one and a half years.

The Master of Technology in IT Leadership is a newly launched programme. Jointly offered by NUS Institute of Systems Science and NUS School of Computing, it is designed to prepare, develop and nurture the next generation of IT leaders for Asia. For more information, go here.










To gain admission to the programme, candidates will be required to meet the following criteria:

a. Possess an undergraduate degree, preferably in Science or Engineering and a grade point average of at least B.

Preferably have 2 years relevant working experience.

MTech SE:

  • Preferably have 2 years relevant working experience as an IT professional in software development or maintenance (e.g. programmer, designer, software project manager).
  • Please note that candidates with highly relevant IT degrees, with consistently good academic records, and good practical software development knowledge gained either through course work, course projects or professional IT certifications may be granted a work experience waiver.

MTech  KE:

  • Preferably have 2 years relevant working experience as an IT professional (e.g. software developer, business analyst) or as a domain expert working in an area where Knowledge Engineering can be applied.
  • Please note that candidates with highly relevant IT degrees, with consistently good academic records, and good practical computing knowledge gained either through course work, course projects or professional IT certifications may be granted a work experience waiver.


  • Preferably have 2 years relevant working experience. IT, engineering and scientific professionals would make ideal candidates. However, those with work experience as domain experts, working in an environment where they can apply Business Analytics, would also be acceptable candidates.
  • Please note that candidates with highly relevant degrees in Mathematics, Statistics, Econometrics, Management Science, Operational Research or similar, with consistently good academic records may be granted a work experience waiver.

Have passed an entrance test administered by ISS.

  • Certain candidates who possess highly relevant Honours/Masters/PhD degrees may be granted entrance test waiver after assessing their application.
  • ISS may, at its discretion, accept GRE general test in lieu of ISS entrance test in genuine cases (eg: a candidate lives in a country where ISS does not administer entrance tests or candidate had valid reasons that prevented him/her from attending the ISS entrance test when it was administered.)
d. Have received a favourable assessment at an admissions interview conducted by ISS.
e. Have a high proficiency in the English language (spoken and written). International applicants who graduated from universities where English is not the medium of instruction may be asked to take TOEFL/IELTS.
  • TOEFL: Paper-based test (580)
                : Computer-based test (237)                 
                : Internet-based test (85)
  • IELTS: Result of 6.0

Note: Institution code of NUS-ISS for TOEFL is 2432


It should be noted that all decisions in the application process are at the discretion of ISS management.









Course Outline

Table A (Part-time)
Table A (Full-time) 
Year 1 Year 2 Year 3
Table B: Basic Elective Courses
Table C : Advanced Elective Units

Table A : Time Table (Part-time)
Year 1
  Software Engineering
Knowledge Engineering
Enterprise Business Analytics
Jan -Mar
Unit 1
Basic SE Discipline
(Core Course)
  • Software Engineering Process
  • Object-Oriented Software Development
Intelligent Systems & Techniques for Business Analytics
(Core Course)
  • Introduction to Knowledge Engineering & Knowledge-based Systems
  • Introduction to Statistical & Machine Learning
Foundations of Business Analytics
(Core Course)
  • Introduction to Business Analytics
  • Business Analytics Techniques
Apr - Jun
Unit 2
Select Two Elective Courses
(See Table B)
Jun Unit 1 & 2 Exams

Jul - Aug
Unit 3

Analysis & Design
(Core Course)
  • Requirements & Analysis
  • Design & Implementation
Data Warehousing for Business Analytics
(Core Course)
  • Data Extract, Transform & Load
  • Data Preparation & Exploration
  • Query & Reporting
  • Data Management
Data Analytics
(Core Course)
  • Introduction to Data Analytics
  • Data Analytics Algorithms & Techniques
Unit 4
Select Two Elective Courses
(See Table B)
Nov Unit 3 & 4 Exams
Year 2
  Software Engineering
Knowledge Engineering
Enterprise Business Analytics
Jan - Mar 
Unit 5
Software Project Management
(Core Course)
  • Project Strategies
  • Planning & Estimating
  • Scope/Risk Management
  • Progress Monitoring & Control
Data Mining Methodology & Methods
(Core Course)
  • Feature Selection & Feature Extraction
  • Classification & Cluster Analysis
  • Building Models
  • Model & Algorithm Evaluation
Advanced Analytics
(Core Course)
  • Predictive Models & Analytics
  • Business Forecasting
Apr - Jun 
Unit 6
Select Two Elective Courses
(See Table B)
Jun Unit 5 & 6 Exams
Jul - Aug 
Unit 7
Software Quality Management
(Core Course)
  • Software Quality Engineering
  • Quality Management Systems: ISO9001 and CMMI
  • Auditing, Peer Reviews & Testing
  • Software Configuration Management
Developing Intelligent Systems for Business Analytics
(Core Course)
  • Problem Understanding & Modeling
  • System Architecture
  • Advanced Techniques & Algorithms
  • System Development & Fine-tuning
Decision Making & Optimization
(Core Course)
  • Problem Formulation
  • Linear/ Goal/ Integer Linear/ Non-linear/ Deterministic Dynamic Programming
Sep - Nov 
Unit 8
Select Two Elective Courses
(See Table B)
Nov Unit 7 & 8 Exams
Year 3
Semester I: Jul - Dec 
Semester II: Jan - Jun 
Units 9-11
Select Three Advanced Elective Units
(See Table C)
Oct/ Nov 
Apr/ May
Unit 9 -11 Exams


Table A : Time Table (Full-time)

Year 1 Semester 1: Students will take Units 1, 2, 5 & 6 from Jan - Jun

Year 1 Semester 2: Students will take Units 3, 4, 7 & 8 from Jul - Nov

Year 2 Semester 1: Students will take 3 Advanced Electives


Table B: Basic Elective Courses

Requirements, Design & Construction

  • Software Requirements Engineering
  • Human Computer Interface
  • Object Oriented Design Patterns
  • Architecting Software Solutions
  • Software Maintenance & Evolution
  • Software Prototyping


Knowledge Engineering Techniques

  • Computational Intelligence I
  • Computational Intelligence II
  • Text Mining
  • Case Based Reasoning
  • Knowledge Management

Advanced IT Management

  • Software Metrics & Process Improvement
  • IT Law
  • Managing IT Outsourcing & Subcontracting
  • Business Process Management
  • IT Service Management
  • Agile Software Project Management
  • Advanced Software Estimation


Business Analytics Techniques

  • Campaign Management
  • Customer Relationship Management
  • Web Analytics
  • New Media & Sentiment Analysis
  • Analytics for Logistics Management
  • Analytics for Tourism & Hospitality
  • Analytics for Pharmaceutical Professionals

Software Development Platforms & Technologies

  • Enterprise .NET I
  • Enterprise .NET II
  • Enterprise Java
  • Enterprise Integration
  • Mobile Wireless Application Development


Technopreneurship & Innovation

  • Research on Advanced IT Topics I
  • Research on Advanced IT Topics II
  • Service Innovation


IT Infrastructure Technology

  • Information System Security
  • Cloud Computing


Note :

  1. MTech KE students must select four basic electives from the Knowledge Engineering Techniques group.
  2. MTech EBAC students must select four basic electives from the Business Analytics Techniques group.



Table C: Advanced Elective Units

Department of Electrical & Computer Engineering

  • Multiprocessor Systems
  • Real-time Systems
  • Neural Networks
  • Pattern Recognition

Note: Appropriately qualified candidates will be able to select from other modules offered by the Electrical Engineering Dept.















School of Computing

  • Software Project Management
  • Computer System Performance Analysis
  • Database Tuning
  • Distributed Systems
  • High Speed & Multimedia Networks
  • Parallel & Distributed Database Systems
  • Network Security & Management
  • Database Security
  • Formal Specification & Design Techniques
  • Systems Security
  • Advanced Neural Networks
  • Integration of IS & Business
  • Global Project Co-ordination
  • Knowledge Systems & Management
  • Information Technology Policies
  • Telecoms & Intenational Networks
  • IT & Supply Chain Management
  • IT & Entrepreneurship
  • Decision Making Technologies
  • Advanced Topics in Database Systems
  • Combinatorial & Graph Algorithms
  • Advanced Modeling & Stimulation
  • Electronic Government
  • Computer Mediated Communications
  • Advanced Natural Language Prcoessing
  • Advanced Topic in Artificial Intelligence
  • Information Technology Outsourcing
  • Information Security Policies
  • Decision Making Technologies

Department of Industrial & Systems Engineering

  • Applied Engineering Statistics
  • Engineering Probability and Simulation
  • Applied Forecasting Methods
  • Decision Analysis
  • Industrial Logistics
  • Systems Modeling and Advanced Simulation
  • Statistical Quality Control
  • Systems Approach to Project Management

Division of Engineering & Technology Management

  • Management of Technological Innovation
  • Marketing of High-Tech Products & Innovations
  • Global Innovation Management
  • Systems Approach to Technology and Innovation Management
  • Corporate Entrepreneurship
  • Analyzing Hi-Technology Opportunities


Institute of Systems Science

  • Formal Methods
  • Software Entrepreneurship
  • Enterprise Architecture



A candidate is evaluated through a combination of coursework, project work and examinations. Candidates are required to complete a three hour examination for each core or elective course.

Candidates failing a core course will be asked to withdraw. Candidates must achieve a minimum average grade across all examinations to be awarded the degree. Candidates who do not fulfil the minimum requirements of the degree may be considered for the award of the postgraduate Diploma in Software Engineering or Knowledge Engineering.


Candidates may be granted exemptions for the examinations of up to four basic electives, provided they have at least the equivalent of an NUS/NTU upper 2nd Class honours degree, and have passed the same or similar subjects at either a masters or PhD level.











Fees & Loans

Software Engineering/ Knowledge Engineering

The following fees (after MOE Subsidy) will be applicable for the Master of Technology in Software Engineering and Knowledge Engineering programmes:

Online Application Fee : S$20.00 Payable only once
Semester Tuition Fees :
S$2,325 (Singapore Citizens)
  S$3,250 (Singapore PRs)
  (a)S$4,950 (International Students with *service obligation)
  (b)S$9,050 (International Students without *service obligation)
Semester Tuition Fees :
S$4,650 (Singapore Citizens)
  S$6,500 (Singapore PRs)
  (a)S$9,900 (International Students with *service obligation)
  (b)S$18,100 (International Students without *service obligation)
Semester Miscellaneous Fees : S$201.52 (Part-time)
  S$305.72 (Full-time)


All fees are in Singapore dollars and are inclusive of GST.

For Singapore Citizens and Singapore Permanent Resident students, the fee amounts quoted here are subsidised by the Singapore government (through the Ministry of Education, MOE) and are exclusive of prevailing GST. The applicable GST is subsidised by the MOE.

The substantial tuition subsidy from the Government of Singapore comes in the form of a MOE subsidy which is administered by the Ministry of Education (MOE) and is offered to all admitted students up to the maximum course duration. Not all students are eligible for the MOE subsidy.

Explanatory notes below on service obligation applicable only to International Students.

* As a self-financing student (i.e., paying tuition fees on your own), please note that you are eligible to apply for service obligation with MOE, provided you have not previously enjoyed MOE subsidy in a graduate programme where the degree is of the same or higher level than what you have been offered here.

If your application for service obligation is successful, the tuition fees applicable to you will be as shown above under (a). You will however be charged the higher fee without MOE subsidy (b) on registration day and adjustments to the fees will be made if your application for service obligation is successful.

The service obligation will require you to work in Singapore-based companies for 3 years upon graduation. Singapore-based companies refer to local and international companies that have a base in Singapore that is registered with the Accounting & Corporate Regulatory Authority (ACRA) as well as companies of such local and international companies registered with ACRA that are based overseas.

Full-time students who require financing for their tuition fees may apply for a loan under the Tuition Fee Loan (TFL)scheme. The maximum loan quantum is 90% of the tuition fees payable by Singapore Citizens for the same course.


Enterprise Business Analytics

The following fees (no MOE Subsidy) will be applicable for the Master of Technology in Enterprise Business Analytics programme:

Online Application Fee : S$20.00 Payable only once
Semester Tuition Fees :
S$7,350 (Singapore Citizens/Singapore PRs/International Students)
Semester Tuition Fees :
S$14,700 (Singapore Citizens/Singapore PRs/International Students)
Semester Miscellaneous Fees : S$201.52 (Part-time)
  S$305.72 (Full-time)


For this unsubsidised programme, current students may apply for the ISS Student Assistance Loan Scheme. Students must be either a Singaporean or Singapore Permanent Resident, and demonstrate sufficient need for this loan. The maximum loan amount is 70% of the total tuition fees remaining to be paid by the applicant. Interest is 0% and the repayment period is within 5 years of graduation.

For more information, email to

The above fees  are at the prevailing GST rates, and may be subject to changes without prior notice.

Any queries about payment, please email to











Projects & Internships

A central element of the MTech programme is the project module. The student projects extend over a period of eight months for full-time students and one year for part-time students.

The projects usually take the form of either a system development (for SE and KE) or a consulting engagement (for EBAC) for a sponsoring organisation (for the part-time students, the sponsor is often the employer of one or more team members). These are real life projects which enable students to apply the tools, techniques and methods they have learned, and develop realistic expectations for their future career. For full-time students, the project can be conducted as a team-based internship if required. The expected student commitment for the project module is 60 man-days per team member.

Sponsoring organisations can also take the opportunity to experience and assess new technologies, techniques and methods. Read the internship brochure to find out more about engaging our students.

Objectives and Learning Outcomes


Project Objectives

  • Design and develop a practical software system
  • Demonstrate technical and management skills by documenting various aspects of the system development
  • Deliver a system that fulfills the requirements of the sponsoring company
  • Acquire hands-on experience in analysing needs of the internship company
  • Provide identifiable benefits to the internship company using KE techniques
  • Practise new technical skills through a real-life consulting engagment
  • Apply tools, methods and techniques learned

Learning Outcomes

  • Conduct a project following a formal incremental approach
  • Engineer solutions using an object-oriented analysis and design method and object-oriented construction technologies
  • Apply project and quality management techniques to deliver a solution that meets user requirements
  • Apply KE techniques in experimenting, prototyping, building, testing and validating an intelligent system to provide business value
  • Apply business analytics methods and techniques to solve identified business problems
  • Plan and execute business analytics projects by understanding business problems, identifying appropriate analytics techniques, and then applying data exploration, model building, testing and validating of results


Samples of Projects by MTech Students and Alumni

Programme Project Name Company Description
Master of Technology (Knowledge Engineering) Knowledge-based System for Marketing C.K. Tang The project team uses knowledge-based techniques to deliver a knowledge-based system to assist C.K. Tang in targeted advertisements for their marketing promotions.
NUS-Bot - This is an entry for 2014 AI for Interactive Digital Entertainment (AIIDE) StarCraft AI Competition. NUS-Bot is an intelligent bot for the real-time strategy game StarCraft. It is equipped with a real-world military operation manual of 'Intelligence Preparation of the Battlefield' (IPB), for iterative spatial (map) and temporal reasoning. 
Semantic Search Engine for Business Licences CrimsonLogic CrimsonLogic's Semantic Search System is a user-friendly and intuitive search system designed to provide enhanced user experience while searching on Singapore Government's business licences website.
Smart Trader Piquant Capital Pte Ltd This project is about using predictive analytics for stock selection in the US market.
Master of Technology (Enterprise Business Analytics) Advanced Analytics for Manufacturing: Improving Printhead Quality Hewlett Packard Print-head is made up of three components named as body, base and THA. A parameter called Die-Tilt is measured to ensure quality standards. The objective of the project is to improve the Print-head quality, define new control specifications for product manufacturing and develop a predictive model to predict the Print-head Die-Tilt range, before the actual measurement readings are available.
Analytics for Sales and Marketing (To qualify leads) A B2B Sales Lead Database Provider A B2B Leads database provider wanted to use analytics and predictive models to cut down the time in their sales team chasing the wrong leads. By selecting key features from existing customer data, we were able to develop propensity-based Lead scoring models. Using techniques such as Random forest and logistic regression, we could help to prioritise sales leads based on propensity scores. We discovered that this technique can increase lead qualification accuracy resulting in higher conversion rates and optimised resource capabilities.
Application of Anlaytics to Measure Value Delivered by Public Distribution System Class Project This project deals with an attempt to predict the quality of Public Distribution System in India analytically.
Cargo Yield Analysis Jurong Port Jurong Port is the only multi-purpose port operator in Singapore handling various General & Bulk Cargo. After 50 years in the business, Jurong Port wants greater visibility into the profit by commodity. Under this project, a robust & flexible framework was built to attribute revenue & costs to commodities. Thereby, enabling computation of the profit by commodity. Predictive analytics was employed to predict throughput of commodities that empowered Jurong Port to make foresighted decisions. This project triggered the management’s decision to use analytics as a key focus area for the organisation.
Data Driven Business Solutions for Finance Industry A Financial Services Company Amidst increasing competition in the electronic payment space, Advanced Analytics can be used as a tool for competitive advantage. In this project, we discovered several insights into customer behaviour during the early stages of the customer with the company. Through this discovery, we were able to build classification models to predict future profitability of customers. This enabled the company to potentially save Marketing spend up to 66% and improve ROI by 200%. Machine learning techniques such as Decision Trees, Neural Networks and K-Nearest Neighbour, Regression were used. The tools used were - SQL, Teradata Warehouse, Tableau, SPSS Statistics and Modeller, MongoDB, Python.
Food Supply Chain Optimisation: Achieving Cost Effectiveness Using Predictive Analytics Supply Chain Industry (Research) Public Distribution System is a flagship welfare programme of the Government of India. It is one of the largest supply chain networks in the world. Our study focuses on applying predictive analytics to aid the central body to keep track of the problem of breach of service level agreement between the two agents of food supply chain. Each breach leads to additional inventory carrying cost. Through this study, we aim to show that aided with such analytics, the network can be made more cost effective. The methods we illustrated in this study are also applicable to commercial supply chains.
Mining Twitter to Detect Critical Events IBM Ireland Social media is becoming the de facto information dissemination agent today and users or "social sensors" are posting, tweeting and blogging about everything happening around them. The objective of this project is to use data from Twitter for early detection of critical incidents like an earthquake, plane crash, violent protests etc. that may disrupt the supply chain. Representing key findings and results real time in a format that can be easily understood by business users is a crucial success criteria. The benefits reaped were risk mitigation against disruption, optimisation of supply chain, enablement of smarter procurement and improving supply chain visibility.
Prediction of a Student's Performance on a Mathematical Problem Class Project This project seeks to improve the Cognitive Tutors models, which could save millions of hours of students' time and effort in learning algebra.
Video Recommendation Using Data Analytics Dailymotion The results of this project was creating an improved video recommendation system using different social media trends. In addition, we created a dashboard showing latest reliable trends that will last longer based on different analytical and text mining methods. We also identified the demographics of the user who might be interested in a particular trend.
Warranty Data Analysis & Quality Performance BI Dashboard IBM Ireland Under high pressure of speed to market, and the complexity in product design in the IT industry, zero defects is difficult to achieve. Therefore, quality management has become a key task to deliver customer satisfaction. This project aims to monitor, manage and improve product quality by analysing product warranty data to increase the visibility and traceability through the company’s worldwide supply chain. It contains two parts: product quality performance dashboard and reliability analysis model, which were conducted through CRISP-DM process and were designed to provide actionable insights to multiple stakeholders such as quality managers, sourcing, and engineers for data-driven decision making.
Master of Technology (Software Engineering) eProcurement System Fujitec Singapore Corporation Ltd The eProcurement System is a B2B web application that enables Fujitec to send all its purchase-related documents electronically over the internet. The company's suppliers are then able to access all the relevant documents through this application.
Enterprise Inventory System Boon Tong Kee This project aims to help Boon Tong Kee to better manage products and supplies to reduce company wastage, increase productivity and streamline supply chain management processes across outlets country-wide.
Event Space Management System ChloroCode Pte Ltd This system allows companies to organise, book, charge and rent their unused space to interested parties.
Mobile Food & Beverage Ordering System for Food Courts and Restaurants MVI Technologies (S) Pte Ltd This is a software solution for food courts and restaurants to minimise the ordering queue and improve their operational efficiency by streamlining the order collection and delivery process.
ParkTap - This is a project initiated by Service Innovation elective students and evolved by Research elective students into a mobile application. This is a proof-of-concept of an experiential computing project to enhance visitor experience.


Our past internship companies include:

  • 2359 Media Pte Ltd
  • Decision Science Agency
  • Jurong Port
  • Rakuten
  • A*STAR Insitute of High Performance Computing
  • ECOXplore Pte Ltd
  • Khoo Teck Puat Hospital
  • SIM University (UniSim) School of Science & Technology
  • Acumen Research Laboratories
  • Elnsights Pte. Ltd.
  • Land Transport Authority
  • Singapore General Hospital
  • Ascenz Solutions
  • Hewlett Packard
  • Lynx Analytics
  • Singapore Press Holdings
  • Autodesk
  • Hwa Chong Institution
  • Marcomax Software Labs
  • SingTel
  • Boehringer Ingelhiem
  • Institute for Infocomm Research
  • MindWave Solutions
  • Sypherlabs
  • Boon Tong Kee Pte Ltd
  • IBM
  • Mobius Innovations
  • UFIS Airport Solutions Pte Ltd
  • Crayon Data
  • IBM Ireland
  • MVI Technologies
  • Ventes Pte. Ltd
  • CrimsonLogic
  • Integral Solutions
  • Panasonic
  • Welcom Real-time Pte Ltd
  • Daily Motion
  • Inland Revenue Authority of Singapore
  • PayPal












Contact Details

For General Enquiries:

Tel: (65) 6516 2093
Fax: (65) 6778 2571

For Postgraduate Programme Enquiries:
Application & Admission

Tel: (65) 6601 3161/ 6516 2516/ 6153/ 7829/ 6769