School of Continuing & Lifelong Education
Master of Science (Industry 4.0)
The MSc (Environmental Management) is a multidisciplinary and interdisciplinary graduate degree programme offered in collaboration with the NUS Business School, College of Design and Engineering, Faculty of Science, Faculty of Law and Lee Kuan Yew School of Public Policy.
In 2022, MEM was identified as a strategic programme and NUS flagship course in sustainability by the University Sustainability and Climate Action Council. In line with this, the programme curriculum was revamped to provide pedagogical flexibility to students whilst providing a broad-based multidisciplinary curriculum.
Both full-time and part-time studies are offered. The period of candidature are as follows:
- Full-time studies can be completed in 12-24 months. The maximum candidature is 24 months.
- Part-time studies can be completed in 24-36 months. The maximum candidature is 36 months.
Candidates must satisfy the following requirements to be conferred the degree of MSc (Environmental Management):
- Read and pass a total of 40 Units, comprising:
- 28 Units in Essential core courses
- 12 Units in Elective courses
- At least 8 Units from Specialised Electives
- Up to 4 Units from General Electives
- Attain minimum GPA of 3.00; and
- Satisfy any other additional requirements that may be prescribed by the Programme Management Committee for MSc (Environmental Management), or the University.
Modules are generally 4 MCs, except when otherwise stated.
- Essential Core Courses (20 Units)
- IND5001 Introduction to Industry 4.0 and Applications
- IND5002 Digital Physical Integration in Industry 4.0
- IND5003 Data Analytics for Sense-making
- IND5004 Digital Infrastructure and Transformation
- IND5005A Professional Career Development (0.5MC)
- IND5005 (4MC) / IND5005B (3.5MC) Industry Consulting and Application Project
- Graduate Certificates & Elective Courses:
All required electives must be completed for the award of the graduate certificate that will be issued by the respective faculties. In addition to the graduate certificate, candidates may select any elective offered to meet the 40-MC graduation requirement.
| Faculty | Course Title | Units |
| FoE |
Graduate Certificate in Additive Manufacturing (Choose 6 courses) | 12 |
| ME5608A Principles and Processes of Additive Manufacturing
ME5608B Hybrid Manufacturing ME5615A Design and Pre-processing for Additive Manufacturing ME5615B Post-processing for Additive Manufacturing ME5614A Special Project in Additive Manufacturing ME5513A Fatigue Analysis for Additive Manufacturing MLE5301 Metallic & Ceramic Materials in Additive Manufacturing MLE5302 Polymer Materials in Additive Manufacturing |
2
2 2 2 2 2 2 2 |
|
| Graduate Certificate in Internet of Things (Choose 5 modules) | 10 | |
| EE5020 Data Science for Internet of Things
EE5021 Cloud Based Services for Internet of Things EE5022 Cyber Security for Internet of Things EE5023 Wireless Networks EE5024 Sensor Networks EE5060 Sensors and Instrumentation for Automation EE5061 Industrial Control and IEC Programming EE5027 Statistical Pattern Recognition EE5026 Machine Learning for Data Analytics EE5025 Intellectual Property: Innovations in IoT |
2
2 2 2 2 2 2 2 2 2 |
|
| Graduate Certificate in Robotics and Automation (Choose 5 courses) | 10 | |
| EE5060 Sensors and Instrumentation for Automation
EE5061 Industrial Control and IEC Programming EE5062 Autonomous Systems EE5063 Modelling of Mechatronic Systems EE5064 Dynamics and Control of Robotic Manipulators EE5065 Tenets of AI in Robotics ME5405A Machine Vision Fundamentals ME5408 Kinematics of Robot Manipulators ME5607 Smart Factories |
2
2 2 2 2 2 2 2 2 |
|
| FoS | Graduate Certificate in Data Mining and Interpretation | 8 |
| ST5227 Applied Data Mining
DSA5203 Visual Data Processing and Interpretation |
4
4 |
|
| Graduate Certificate in Deep Learning for Industry | 8 | |
| DSA5102 Foundations of Machine Learning
DSA5204 Deep Learning and Applications |
4
4 |
|
| Graduate Certificate in Quality Assurance and Yield Optimization (Choose 2 courses) | 8 | |
| ST5203 Design of Experiments for Product Design and Process Improvements
ST5208 Analytics for Quality Control and Productivity Improvements ST5212 Survival Analysis ST5210 Multivariate Data Analysis |
4
4 4 4 |
|
| BIZ | Graduate Certificate in Digital Supply Chain | 12 |
| IND5021 Managing the Digital Supply Chain
IND5022 Data Analytics for Smart Manufacturing DOS5101A Managing the Financial Supply Chain IND5024 Strategic Procurement in a Digital World |
4
4 2 2 |
|
| SoC | Graduate Certificate in Principles and Practice of Secure Systems (Choose 3 courses) | 12 |
| CS5322 Database Security
CS5332 Biometric Authentication CS5331 Web Security CS5321 Network Security CS5439 Software Security |
4
4 4 4 4 |
|
| Graduate Certificate in Digital Business (Choose 3 courses) | 12 | |
| IS5007 Strategising for Global IT-enabled Business Success
IS5116 Digital Entrepreneurship IS5117 Digital Government IS5151 Information System Security Policy and Management |
4
4 4 4 |
|
| ISS |
Graduate Certificate in ISY5002 Pattern Recognition Systems | 13 |
| Graduate Certificate in ISY5004 Intelligent Sensing Systems | 10 |
One intake is admitted every year to start in Semester 1 (i.e. August) of the academic year. The recommended study schedule for full-time and part-time studies are illustrated as below.
| Full-time Study Schedule for AY2019/20 intake | |
| 1st Year of studies, Sem 1: | Core Modules (12 MCs) Preallocated three 4-MC modules Elective Modules (8 MCs) |
| 1st Year of studies, Sem 2: | Core Modules (8 MCs) Preallocated one 4-MC and Capstone modules Elective Modules (12 MCs) |
| Part-time Study Schedule for AY2019/20 intake | |
| 1st Year of studies, Sem 1: | Core Modules (12 MCs) Preallocated three 4-MC modules |
| 1st Year of studies, Sem 2: | Core Modules (4 MCs) Preallocated one 4-MC module Elective Modules (8 MCs) |
| 1st Year of studies, Special Term Part 1 or 2nd Year of studies, Sem 1: | Core Modules (4 MCs) Preallocated Capstone module |
| 2nd Year of studies, Sem 1: | Elective Modules (4 MCs) Select from 2-MC and 4-MC modules |
| 2nd Year of studies, Sem 2: | Elective Modules (8 MCs) Select from 2-MC and 4-MC modules |
| Full-time Study Schedule for AY2020/21 intake onwards | |
| 1st Year of studies, Sem 1: | Core Modules (12.5 MCs) Preallocated three 4-MC and one 0.5-MC modules Elective Modules (8 MCs) |
| 1st Year of studies, Sem 2: | Core Modules (7.5 MCs) Preallocated one 4-MC and Capstone modules Elective Modules (12 MCs) |
| Part-time Study Schedule for AY2020/21 intake onwards | |
| 1st Year of studies, Sem 1: | Core Modules (3.5 MCs) Preallocated three 4-MC and one 0.5-MC modules |
| 1st Year of studies, Sem 2: | Core Modules (4 MCs) Preallocated one 4-MC module Elective Modules (8 MCs) |
| 1st Year of studies, Special Term Part 1 or 2nd Year of studies, Sem 1: | Core Modules (3.5 MCs) Preallocated Capstone module |
| 2nd Year of studies, Sem 1: | Elective Modules (4 MCs) Select from 2-MC and 4-MC modules |
| 2nd Year of studies, Sem 2: | Elective Modules (8 MCs) Select from 2-MC and 4-MC modules |