This course will enhance the knowledge and skills of participants and enable them to undertake engineering functions in the field of Industrial Internet of Things (IIOT) in the electronics industry.
Upon graduating from the course, you will acquire skills and techniques essential in developing smart solutions through IOT technologies, manage and analyse IIOT data for the system to gain value through informed intelligent decisions. You can gain employment in the following specialisation areas that are required by companies at various stages of IIOT and Industry 4.0 adoption:
Engineering Analytics and Machine Learning
Smart Sensors and Devices
This course is eligible for SkillsFuture Study Award. For more information, please visit the website below.
At least a Polytechnic Diploma or ITE Technical Diploma/Technical Engineer Diploma/Work-Learn Technical Diploma in Engineering, or University Degree in Engineering or Physical Science, or equivalent.
Applicants who do not meet the entry requirements may be considered for admission to the course based on evidence of at least 5 years of relevant working experience or supporting evidence of competency readiness. Suitable applicants who are shortlisted may have to go through an interview and/or entrance test. The Polytechnic reserves the right to shortlist and admit applicants.
|ECMC201||Smart Sensors and Devices
The subject provides knowledge and skills to enable sensors and devices to become smart and connected. The learner will be exposed to the use of embedded systems to enable processing and data reduction to reduce communication. Data communication technologies for information exchange with other devices will also be covered. The learner will develop and test a smart sensor and device network for typical industrial application scenarios (eg. predictive maintenance, process optimization).
The subject covers knowledge and skills essential for integrating heterogeneous subsystems into a smart system. The subject will adopt a systems engineering approach to examine current and emerging trends, key techniques and strategies for developing system and network integration solutions. Students will be exposed to integration challenges such as legacy integration, human-system integration and system of system integration. The subject will cover knowledge and skills on commonly used IOT protocols and industrial connectivity standards and fieldbuses, as well as relevant hardware and software interfaces suitable for such integration. A mini-project will provide opportunity for the students to apply their learning to integrate heterogeneous subsystems.
|ECSE201||IIOT Data Management
This subject seeks to provide students with the knowledge and skills in managing structured, unstructured and semi-structured data generated by an IIOT system using appropriate cloud, database and layered databus technologies. It provides exposure to the need for alternative database management systems beyond traditional relational database systems in managing IIOT data. The subject covers data acquisition, data reduction, storage and retrieval for big data processing. These would provide the basis for analytics, machine learning, visualization, reports and alerts.
|ECSE202||Engineering Analytics and Machine Learning
This subject provides knowledge of the concepts and skills in the tools used in data analytics and machine learning. It provides exposure to the process of data gathering, extraction and visualization. The subject covers the various stages of data analytics, from gathering data, asking the right questions to analysing and interpreting data to identify patterns and trends, that lead to intelligent actionable recommendations. The subject includes the deployment of machine learning models and algorithms, for e.g. predictive maintenance and big data-driven quality control.
This subject seeks to provide students with the knowledge and skills to integrate and apply relevant knowledge to a work-based problem or scenario while demonstrating appropriate project development skills, professional ethics and attitudes. The deliverable may be a solution to a problem, process /product improvements, development, design, evaluation or implementation of ideas.
Modes of Assessment
- Class participation
- Class tests
|Intake Info||Application Closing Date||Course Duration|
Commencing 19 October 2020
Max 3 times a week, 7pm - 10pm on weekdays
04 May 2020 - 20 July 2020
|Fee Type||PDC in IIOT Integration
|PDC in Engineering Analytics
|SME-sponsored Singapore Citizens & Permanent Residents||S$251.52||S$377.28|
|Singapore Citizens aged 40 & above||S$243.96||S$365.94|
|Singapore Citizens aged below 40||S$359.52||S$539.28|
|Singapore Permanent Residents||S$975.84||S$1,463.76|
|Others & Repeat Students||S$2,439.60||S$3,659.40|
SkillsFuture Credit Approved. For more details, please click here.
Course fees payable is based on per MC.
Course fees will be reviewed by MOE on an annual basis and adjusted accordingly.
MOE subsidy will not be applicable for students who repeat a module or semester.
With effect from 1 Jul 2020, the Workforce Training Scheme (WTS) will be replaced by Work Support Scheme (WSS), for more information, please visit:
8.30 am to 6.00 pm (Mon to Thu)
8.30 am to 5.30 pm (Fri)
Closed on Sat, Sun & Public Holidays
- Website: https://www.tp.edu.sg/tsa
Temasek SkillsFuture Academy
East Wing Block 1A,
Level 3, Unit 81
21 Tampines Ave 1
Temasek Polytechnic reserves the right to alter the course, modify the scale of fee, amend any other information or cancel course with low enrolment.