Internet of Things and Data Analytics for Industry

Internet of Things and Data Analytics for Industry

Date: 13 - 15 July 2020
Time: 9am - 1pm
Venue: Online Training - Zoom

Singapore Water Association will be organising a 3-Day online training course on Internet of Things and Data Analytics for Industry on 11-13 May 2020 at e2i. This programme is supported by Employment and Employability Institute Pte Ltd (“e2i”).


This short course will be conducted fully online using Zoom, a professional video-conferencing application that provides good video and audio quality, and computer screen sharing features.

The experience for participants will be similar to attending the course in-person, without the hassle of travelling to the course venue. The sessions will be interactive. Participants can raise their hands and ask questions and group discussions can be carried out.  A login password will be sent to the registered participants upon confirmation.


Decent laptop or computer with webcam, headset (microphone and headphones) and a good Internet connection (e.g. you are able to watch YouTube smoothly).


Complete the Registration Form by 30 April 2020

Limited to 20 slots only!

COURSE FEES (excluding GST 7%)

Course Fee

SWA Member


Full Fee

SGD 1,250.00

SGD 1,380.00

E2i course fee subsidy*

SGD    180.00

SGD    180.00

 Special online discount

  SGD 200.00

  SGD 200.00

Nett Course Fee

  SGD 870.00

  SGD 1000.00

* Eligibility for e2i Funding:

  • Employed Singaporean or Singapore PRs who are self-sponsored or employer-sponsored
  • Achieve at least 75% attendance
  • The participants and/or the Company shall not receive any other funding from government sources

Award of Certificate & PEB Programme

  1. A Certificate of Completion will be awarded to participants who meet the 75% attendance.
  2. This course is qualified for PDUs by PEB (PDUs to be confirmed).


The objectives of this course are to enable participants to gain a working knowledge about the Internet of Things (IoT) and its potential to transform industrial sectors due to its ability to provide real-time visibility into operational processes. It also covers data analytics which enables trends to be identified and anomalies to be detected so that organisations can respond appropriately. A hands-on session provides participants with the opportunity to put into practice the concepts learnt. These are essentials skills these days as more organisations harness the benefits of digitalisation.


  • Be informed about the state-of-the-art in IoT devices and techniques
  • Understand the key technological building blocks of IoT devices, such as sensors, wireless networking, embedded and distributed processing and energy considerations
  • Understand IoT data aggregation and analytics using edge, fog and cloud computing
  • Appreciate the features found in modern IoT data processing platforms
  • Learn about data analytics algorithms for analysing real-time IoT data and data sets
  • Gain experience in applying data analytics algorithms on IoT data to develop actionable insights based on an industry case study


  1. Introduction to the Internet of Things (IoT)

– Sensors and smart meters

– Wireless communication protocols: e.g. LoRa, NB-IoT

– Information processing at the edge and cloud; digital twin

– Energy considerations

– Industry case study

  1. IoT and Data Platforms

– Data collection, storage, query, analysis and applications (platform examples: ThingWorx, MindSphere, Azure)

  1. Data Analytics for IoT Sensor Data

– Data analytics techniques: regression, clustering, classification, including support vector machines (SVM)

– Data modelling of time-series sensor data

– Anomaly detection from time-series sensor data, with application in predictive maintenance

  1. Hands-on Exercises

– Participants will analyse sensor data using software to derive insights and perform anomaly detection. The necessary data sets and computer code will be provided.


Tham Chen Khong is an associate professor at the Department of Electrical and Computer Engineering (ECE) of the National University of Singapore (NUS). His research and training activities are in the areas of sensor data analytics, wireless mobile computing and machine learning algorithms and architectures. Other than NUS, he has worked at A*STAR Institute for Infocomm Research (I2R) and Accenture. He has led a number of research projects funded by NRF, A*STAR and MOE, Singapore, as well as consultancy projects with industry. He obtained his Ph.D. and M.A. degrees in Electrical and Information Sciences Engineering from the University of Cambridge, United Kingdom, and is a senior member of the IEEE.