NUS
 
ISS
 

Master of Technology in Enterprise Business Analytics

Overview

Next Intake Aug 2024 (Full-Time), Jan 2025 (Part-Time)
Duration
  • Full-time 1 year (2 semesters)
  • Part-time 2 years (4 semesters)
Application Timeline Admissions into the MTech programme is competitive. Eligible students will be offered admissions on a first-come first-served basis.

Applications for August 2024 admissions into full-time MTech EBAC should be submitted before 30 April 2024.
Applications for January 2025 admissions into the part-time MTech EBAC should be submitted before 15 October 2024.

*The dates above are subjected to changes.
Entrance Test / GRE Face-to-Face: 23 May 2024

*Applicants based in Singapore are to take the entrance test in NUS-ISS. Applicants based overseas are required to submit their GRE scores in place of the Entrance Test. Please refer to the section "Admission & Application" for further details. 

The dates above are subjected to changes. 

Download Brochure English
Info-session Click here for dates
Enquiry iss-admissions@nus.edu.sg  

The NUS Master of Technology in Enterprise Business Analytics programme (MTech EBAC) is meticulously crafted to meet the industry's pressing need for adept data scientists and machine learning specialists, with a keen focus on their role in advancing enterprises through the power of data and AI. Our program is a gateway to specialist, expert and leadership positions in data science.

MTech EBAC stands out by offering holistic training in key aspects of data science and AI, from exploratory data analysis and predictive modelling to natural language processing, recommender systems and big data engineering. Professionals will be equipped with the essential technical expertise to harness data, machine learning and enabling technologies to develop and deploy effective data-driven solutions. These solutions are designed not only to enhance decision-making processes but also to optimize operations, drive profitability and elevate overall user experiences within enterprises.

Recognition:

  • Top student is awarded the IBM Medal and Book Prize
  • Best Project Prize

Graduates of the programme will be capable of undertaking tasks such as:

  • Discovering insights from data
  • Applying concepts and techniques to solve major business problems
  • Designing and customizing marketing campaigns through efficient targeting
  • Analysing sales channels
  • Optimising the marketing mix of their organisations
  • Improving decision-making to increase returns on investments for their organisations
  • Predicting the future profitability of their organisations
  • Automate production fault detection in manufacturing using predictive modelling

Learning outcomes:

  • Help enterprises move towards a stronger emphasis on computer tools and statistical and machine learning techniques to develop high-performance analytics capability
  • Translate massive and complex unstructured data (e.g. text) into insights
  • Produce predictive models to solve a broad range of problems across various business functions and units
  • Contribute to the development of more effective business strategies and plans for sustainable growth and competitive advantage

Scholarship:

  • IMDA Post Graduate Scholarship: 

    The SG Digital Scholarship (Postgraduate) is an industry scholarship that empowers students pursuing tech or media-related studies at the Masters or PhD level. Individuals pursuing postgraduate studies in specialised tech or media-related areas such as Artificial Intelligence, Quantum Technologies, Immersive Media, and Film Studies can chart their future with this scholarship. Scholarship details and eligibility criteria can be found here.




Courses

MTech EBAC candidates must successfully complete the following course components to be awarded the degree:

  • Fundamental - Complete 2 Graduate Certificates
  • Specialist - Complete 2 of 5 Graduate Certificates

Fundamental Analytics Areas

Analytics Project Management

Students will be equipped with practice-oriented data analytics skills and knowledge in managing analytics project. Participants will be equipped with essential skillsets to understand analytics processes and best practices, to manage data and resources, to understand structure of analytics solution, to perform data visualisation, to present insights via compelling data storytelling, and to ensure successful implementation of analytics project.

Courses:
  • Statistics for Business II
  • Data Storytelling
  • Data Management for Analytics
  • Managing Business Analytics Projects

Business Analytics Practice

Students will learn the foundation skills to understand, design and solve analytics problems in the industry involving structured and unstructured data. It is a course which prepares the participants to embark upon the journey to become a data scientist in due course.

Courses:
  • Data Analytics Process and Best Practices II
  • Statistics Bootcamp II
  • Predictive Analytics – Insights of Trends and Irregularities
  • Text Analytics

 

Specialist Analytics Areas

Customer Analytics

Students will be equipped with the skills to manage the customer data and build analytics solutions for customer relationship management. The course will enable them to apply techniques for targeted customer marketing, to reduce churn, increase customer satisfaction and loyalty and increase profitability.

Courses:
  • Customer Analytics
  • Advanced Customer Analytics
  • Campaign Analytics

Big Data Analytics

Students will learn various aspects of data engineering while building resilient distributed datasets. Participants will learn to apply key practices, identify multiple data sources appraised against their business value, design the right storage, and implement proper access model(s). Finally, participants will build a scalable data pipeline solution composed of pluggable component architecture, based on the combination of requirements in a vendor/technology agnostic manner. Participants will familiarize themselves on working with Spark platform along with additional focus on query and streaming libraries.

Courses:
  • Big Data Engineering for Analytics
  • Recommender Systems
  • Processing Big Data for Analytics

Practical Language Processing

Students will be taught advanced skills in practical language processing. This includes fundamental text processing, text analytics, deep learning techniques and their application in sentiment mining and chatbots development.

Courses:
  • Text Analytics
  • New Media and Sentiment Mining
  • Text Processing using Machine Learning
  • Conversational UIs

Specialised Predictive Modelling & Forecasting

Students who complete this certificate will have skills in advanced predictive, prescriptive & forecasting techniques applicable in the areas of health, government and many other domains. The topics include advanced predictive and forecasting techniques, survival analysis, experimental design techniques, econometric forecasting, mathematical optimisation methods etc.

Courses:
  • Complex Predictive Modelling & Forecasting
  • Product & Pricing Analytics
  • Analytics for Commercial Excellence

Intelligent Financial Risk Management

Students will focus on extracting knowledge for decision making from financial data which involves analysing and gaining insights from financial data sources. It also explores augmenting customer information using alternative data sources to complement decision making for financial services (banks, insurance, non-banking financial related services, etc.).

Courses:
  • Advanced Machine Learning for Financial Services
  • Explainable & Responsible AI for Finance
  • Credit Risk Modelling and Analytics
  • Alternative Data for Fintech Innovation

 



Learning Journey

 

Fundamental
(Complete 2 Graduate Certificates)
Specialist
(Complete 2 of 5 Graduate Certificates)
 

Analytics Project Management

Statistics for Business II
Data Story Telling
Data Management for Analytics 
Managing Business Analytics Projects
Graduate Certificate in Analytics Project Management
 

Business Analytics Practice

Statistics Bootcamp II
Data Analytics Process and Best Practice II
Predictive Analytics – Insights of Trends and Irregularities
Text Analytics
Graduate Certificate in Business Analytics Practice
 

Customer Analytics

Customer Analytics
Advanced Customer Analytics
Campaign Analytics
Graduate Certificate in Customer Analytics
 

Big Data Analytics

Big Data Engineering for Analytics
Recommender Systems
Processing Big Data for Analytics
Graduate Certificate in Big Data Analytics
 

Practical Language Processing

Text Analytics
New Media and Sentiment Mining
Text Processing using Machine Learning
Conversational UIs
Graduate Certificate in Practical Language Processing
 

Specialised Predictive Modelling & Forecasting

Complex Predictive Modelling & Forecasting
Product & Pricing Analytics
Analytics for Commercial Excellence
Graduate Certificate in Specialised Predictive Modelling & Forecasting
 

Intelligent Financial Risk Management

Advanced Machine Learning for Financial Services
Explainable & Responsible AI for Finance
Credit Risk Modelling and Analytics
Alternative Data for Fintech Innovation
Graduate Certificate in Intelligent Financial Risk Management


Capstone Project & Internships

A central element of the MTech programme is the project module. 

Student projects for MTech EBAC students will include intense full time engagement of 5 months with companies for full time students. For part-time students the capstone engagement will be for 7-12 months. Students are allowed to conduct their project as a team-based internship if desired. The expected commitment for the project is 30 man-days per team member. In addition, students will need to attend a mandatory 4 day course - Data Science Solutions Implementation.

Objectives

  • Practise new technical skills in a real industry environment
  • Apply tools, methods and techniques learnt

Learning outcomes:

  • 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 
Read more on Internship & Placements

Timetable & Exams

Timetable & Exams for Full-time Students

Year

Curriculum

Assessment

Year 1

Semester 1 (Jul - Nov)

EBA5001: Analytics Project Management (compulsory)

  • Statistics for Business II
  • Data Storytelling
  • Data Management for Analytics
  • Managing Business Analytics Projects

EBA5002: Business Analytics Practice (compulsory)

  • Data Analytics Process and Best Practice II
  • Statistics Bootcamp II
  • Predictive Analytics – Insights of Trends and Irregularities
  • Text Analytics
  • Continuous assessments
  • Open book written exams

Year 1

Semester 2 (Jan - Mar)

Choose ONE

  • Continuous assessments
  • Open book written exams

EBA5003: Customer Analytics

  • Customer Analytics
  • Advanced Customer Analytics
  • Campaign Analytics

 

EBA5006: Big Data Analytics

  • Big Data Engineering for Analytics
  • Recommender Systems
  • Processing Big Data for Analytics

Choose ONE

  • Continuous assessments
  • Open book written exams

EBA5004: Practical Language Processing

  • New Media and Sentiment Mining
  • Text Processing Using Machine Learning
  • Conversational UIs

EBA5005: Specialised Predictive Modelling and Forecasting

  • Complex Predictive Modelling & Forecasting
  • Product & Pricing Analytics
  • Analytics for Commercial Excellence

EBA5008: Intelligent Financial Risk Management

  • Advanced Machine Learning for Financial Services
  • Explainable & Responsible AI for Finance
  • Credit Risk Modelling and Analytics
  • Alternative Data for Fintech Innovation

Year 1

Semester 2 (Mar - Jul/Aug)

Team-based Internship or Off-site Project

Hands-on project with external organisation

  • Project, presentation & report
 

Timetable & Exams for Part-time Students

Year

Curriculum

Assessment

Year 1

Semester 1 (Jan - May)

EBA5001: Analytics Project Management (compulsory)

  • Statistics for Business II
  • Data Storytelling
  • Data Management for Analytics
  • Managing Business Analytics Projects
  • Continuous assessments
  • Open book written exams

Year 1

Semester 2 (Jul - Nov)

EBA5002: Business Analytics Practice (compulsory)

  • Data Analytics Process and Best Practice II
  • Statistics Bootcamp II
  • Predictive Analytics – Insights of Trends and Irregularities
  • Text Analytics
  • Continuous assessments
  • Open book written exams

Year 2

Semester 1 (Jan - May)

Choose ONE

  • Continuous assessments
  • Open book written exams

EBA5003: Customer Analytics

  • Customer Analytics
  • Advanced Customer Analytics
  • Campaign Analytics

EBA5006: Big Data Analytics

  • Big Data Engineering for Analytics
  • Recommender Systems
  • Processing Big Data for Analytics

Year 2

Semester 2 (Jul - Nov)

Choose ONE

  • Continuous assessments
  • Open book written exams

EBA5004: Practical Language Processing

  • New Media and Sentiment Mining
  • Text Processing Using Machine Learning
  • Conversational UIs

EBA5005: Specialised Predictive Modelling and Forecasting

  • Complex Predictive Modelling & Forecasting
  • Product & Pricing Analytics
  • Analytics for Commercial Excellence

EBA5008: Intelligent Financial Risk Management

  • Advanced Machine Learning for Financial Services
  • Explainable & Responsible AI for Finance
  • Credit Risk Modelling and Analytics
  • Alternative Data for Fintech Innovation 

Year 2

Semester 1-2 (Mar - Sep)

Team-based Internship or Off-site Project

Hands-on project with external organisation

  • Project, presentation & report
 

Students are evaluated through a combination of course work, project work and examinations. All students are required to complete a three-hour examination for each fundamental and specialist module taken.


Fees


Fee Component Singapore Citizens Singapore Permanent Residents International Students
Full Tuition Fees S$50,060 to S$54,500 S$50,060 to S$54,500 S$50,060 to S$54,500
NUS-ISS Subsidy S$6,392 to S$7,280 S$6,392 to S$7,280 -
Nett Tuition Fees S$43,668 to S$47,220 S$43,668 to S$47,220 S$50,060 to S$54,500
9% GST on Nett Tuition Fees S$3,930.12 to S$4,249.80   S$3,930.12 to S$4,249.80 S$4,505.40 to S$4,905
Total Nett Tuition Fees, including GST  S$47,598.12 to S$51,469.80 S$47,598.12 to S$51,469.80 S$54,565.40 to S$59,405
NUS-ISS Study Award for AY2024/AY2025
(See T&Cs on Study Award below)
Up to S$15,000 Up to S$7,500 -
Total Nett Tuition Fees payable after Study Award, including GST S$32,598.12 to S$36,469.80 S$40,098.12 to S$43,969.80 S$54,565.40 to S$59,405
  
Note:
    1. NUS-ISS provides a subsidy of 20% of the component course fees for Singapore Citizens and Singapore Permanent Residents. 
    2. The NUS-ISS Fees and Subsidy are subjected to change without prior notice and there is no subsidy for the Practice Module fees or the Capstone fees. 
    3. From AY2024/2025, NUS-ISS will extend a 20% subsidy for NUS Alumni and no other subsidy shall apply concurrently. 
    4. All study awards and subsidies are only for eligible self-paying students. 
    5. The exact tuition fees will be calculated based on the student’s selection of the Graduate Certificates. 
    6. The miscellaneous fees payable is set out here
    7. With effect from 1 January 2024, individuals using credit/debit card and eWallet to make payment via NUSFastPay will have to pay a 1% processing fee.
    8. Starting from AY2024, we will be collecting a non-refundable and non-transferable acceptance fee of S$3,270 (inclusive of prevailing GST), which will be credited towards your tuition fees, upon acceptance of offer.
    9. Please note that the fees mentioned on this MTech webpage do not include SSG (SkillsFuture Singapore) funding. Students who wish to confirm the availability of SSG funding for individual courses are encouraged to refer to the respective course fees or reach out to the iss-admissions@nus.edu.sg for further assistance.

What Do Miscellaneous Fees Cover?

Miscellaneous fees are typically levied on items that are either not covered or partially covered by tuition fee and grant/subsidy. All students, whether registered on a full-time or part-time basis, are charged the mandatory miscellaneous fees. These are due at the same time as the tuition fees. These fees help defray the costs of student activity, health services and insurance, campus shuttle service and other services.

Any queries about fees and payment, please contact us at issfinance@nus.edu.sg.

Loans and Subsidies

Students who require financing for their tuition fees may apply for the following:

  • Tuition Fee Loan

Read up more on the above loans and subsidies


Terms & Conditions of the NUS-ISS MTech Study Award (as of 2024):
  1. The NUS-ISS MTech Study Award will be given to qualifying Singapore Citizens and Singapore Permanent Residents matriculated from AY2023/2024 Semester 2 onwards for the unfunded courses of MTech EBAC degree.
  2. The NUS-ISS MTech Study Award may be amended at any time at the discretion of NUS-ISS.
  3. The quantum of the Study Award:

     Citizenship   Study Award 
     Singapore Citizens   Up to $15,000 
     Singapore Permanent Residents   Up to $7,500 


  4. The Study Award will be calculated based on the Graduate Certificate the student is enrolled to per semester.
  5. The Study Award may not exceed the fees paid by the respective student in that semester.
  6. Any course waivers are subjected to approval and fees would be adjusted accordingly.
  7. The Study Award will be applied after the NUS-ISS subsidy is deducted from the course fees. Please note that the Study Award will not be applied to courses funded by SkillsFuture Singapore (SSG) scheme. This ensures clarity in the application of the Study Award and the alignment with the guidelines set forth by the respective funding bodies. 
  8. The Study Award will be automatically granted to all eligible students upon their matriculation into the respective MTech programme. 
  9. Students have the option to decline the Study Award, and they should communicate this decision to NUS-ISS when accepting entry into the programme. 
  10. Those who are in receipt of partial scholarship/sponsorship shall have proportionally adjusted study awards.
  11. NUS-ISS reserves the right to terminate the Study Award if a scholarship holder's progress or behaviour is deemed unsatisfactory, based on the institute's discretion. 

Admission & Application

Admissions Criteria: 

  • Bachelor's degree preferably in  Mathematics, Statistics, Econometrics, Management Science, Operational Research, Science or Engineering and a grade point average of at least B
  • Demonstrate proficiency in the English Language (written and spoken)*
  • An acceptable GRE score (overseas applicants) or pass NUS-ISS Entrance Test
  • Have received a favourable assessment at admissions interview conducted by NUS-ISS
  • Preferably two years of relevant working experience
  • The NUS-ISS Entrance Test or GRE and interview requirements will be waived for applicants with relevant Bachelor's degrees from NUS, NTU, SMU and SUTD with Second Upper or above Honours.
  • NUS-ISS Graduate Diploma in Systems Analysis alumni who wishes to apply to NUS-ISS Master of Technology programmes may be waived of Entrance Test/GRE and interview requirement if they meet the GPA's requirements*
  • Admission is on competitive basis; eligible students will be offered admisions on a first-come first-served basis. 
GRE / NUS-ISS Entrance Test:
  • International applicants residing overseas are required to submit GRE as evidence to demonstrate their academic capability:
    • A minimum GRE score of 320 (verbal & quantitative) and 3.5 (analytical) is recommended, within 5 years validity. (GRE institution code: 0677)
    • Applicants who have significant work experience relevant to their intended area of study may be considered for admission even if they do not meet the recommended GRE scores. Please note that school projects, internships and enrichment programmes do not count as work experience.
  • Applicants residing in Singapore will be required to pass an Entrance Test administered face-to-face in NUS-ISS
    • Local applicants may opt to submit GREs instead of taking the Entrance Test in which case the same conditions apply as above.
    • A sample entrance test paper can be found here.

Work Experience:

  • Preferably two years of relevant working experience in fields like
    • IT, engineering and various scientific domains
    • Applicants with highly relevant degrees in Mathematics, Statistics, Computer Science, Econometrics, Operations Research or similar, with consistently good academic records may be granted a work experience waiver

*English Language Proficiency

  • Applicants who graduated from universities where English is not the medium of instruction should submit TOEFL (Test of English as a Foreign Language) or IELTS (International English Language Testing System) score as evidence of their proficiency in the English language.
  • TOEFL

    Paper-based test (580)
    Computer-based test (237)
    Internet-based Test (85)
    *TOEFL iBT Home Edition is not accepted.

    IELTS

    Result of 6.0

    • Institution code of NUS-ISS for TOEFL is 2432
    • TOEFL and IELTS are only valid for two years and five years respectively after the test and the validity should not expire before the beginning of the application period for the coursework programme.
    • NUS only accepts TOEFL iBT scores from a single test date, not MyBest scores.

    How to Apply

    All applicants are required to submit an online application for our graduate coursework programme (through-train).  

    Step 1:     You can refer to our detailed step-by-step guide on how to complete the online application.

    Step 2:     It will take you about 30 minutes or more to complete your application.  You will need the softcopies of the supporting documents for your online application.  Click here for the supporting documents to be uploaded and additional information required.

    Step 3:     You can proceed to apply online.  Remember to upload all the required supporting documents under the “Documents Upload” section before you do the online submission.  You can refer to our FAQ.

    Step 4:     Please ensure you submit your online application(s) and make online payment for the application fee (non-refundable) of S$50.00 per application (exclude prevailing GST).

     Important: 

    1. Applications that are incomplete, including missing supporting document(s), will not be processed.
    2. Applicants who are found to have given inaccurate or false information will be required to withdraw from the programme.
    3. All payments for application fee are non-refundable.
    4. Please note that the University has not engaged any external agencies to undertake student recruitment on its behalf. Candidates interested in our graduate programmes are advised to apply directly to the University and not through any agents. Candidates who apply through agents will not have any added advantage in gaining admission and the University reserves the right to reject such applications without giving reasons.


Career Pathways

Find your fit with new opened doors

There is opportunity in Singapore in almost all industries which are rapidly working towards digital transformation and data analytics sits in the heart of it. What you learn in terms of analytics skills is not as important as what you do with it. It is the attitude and the ability to learn from mistakes, and to contribute back to the company that you work for that is likely to make more of a difference than specific analytics skills.

There are two main paths for advancement in analytics - either technical or managerial. Technical means you continue to deepen your technical area in a domain (Finance, Government, Manufacturing, Telecom, Transportation, Technology companies etc.) and you become an expert data scientist in those areas. The other is managerial, where you can focus on designing solutions for clients (internal or external) to achieve their organisation goals in the areas of profit maximisation, automation or digitisation.

Our internship companies often tell us that if we can give them good students as interns, it is very likely they will get a job offer at the end of the internship.

As an MTech EBAC graduate, you will be prepared for specialist, expert and leadership roles in enterprise business analytics to create business value through strategic use of data, visualisation methods, modelling techniques and frontline tools.

Career Prospects

  • Business Analytics Manager
  • Data Scientist and Architect
  • Business Analyst
  • Optimisation Strategy Consultant
  • Business Intelligence and Performance Management Consultant
  • Enterprise Intelligence Manager
  • Market Intelligence Analyst
  • CRM Data Analyst
  • Risk Analyst
  • Marketing Analyst
  • Big Data Analyst

MTech alumni are pursuing their careers at these global organisations:

  • Accenture
  • Creative Technology
  • DBS Bank
  • Defence Science & Technology Agency
  • Deutsche Bank AG
  • Fuji Xerox Asia Pacific
  • HP Singapore
  • IBM Singapore
  • Infocomm Development Authority of Singapore
  • Inland Revenue Authority of Singapore
  • Jurong Port
  • Microsoft
  • Murex
  • NCS
  • NEC Asia Pacific
  • NTUC
  • OCBC Bank
  • Revolution Analytics
  • Singapore Telecommunications
  • Standard Chartered Bank
  • Starhub
  • ST Electronics
  • Tata Consultancy Services

The NUS-ISS Career Services Office helps students to match jobs based on their skills and experience. There will be bi-yearly Career Fairs held for students and graduates to network with employers. However, successful employment will depend on the employers.

The average starting salary of an analytics professional depends on the degree and your previous working experience. For fresh graduates with no work experience, the starting salary ranges from S$4,000 to S$4,500. Graduates with more than 3 years of work experience can expect a starting pay of S$6,000 and above.

The most important skill is to get the job done and be persistent. You need to be broad-based and the technology does not matter.

You can get some salary benchmarks from these sites:

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