One-Day Course 22 May 2020 – Online Learning Via Zoom

About the Instructor

Associate Professor Tham Chen Khong 

Course Description

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.

Mode of Delivery

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.


Decent laptop or computer with webcam, headset (microphone and headphones) and a good Internet connection.


A separate registration is required for each participant.

Learning Outcome

~ 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 and embedded systems

~ Understand IoT data aggregation and analytics using edge and cloud computing

~ 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

Who Should Attend

Executives, engineers and managers who work with or plan to deploy IoT and data analytics technologies, particularly for anomaly detection and prediction

Course Outline

1. Introduction to the Internet of Things (IoT)

~ Sensors, smart meters and IoT wireless networks

~ Information processing at the edge and cloud, and ‘digital twin’

2. Data Analytics for IoT Sensor Data

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

~ Anomaly detection and prediction from time-series sensor data, with application in predictive maintenance

3. Hands-on Exercises

~ Participants will analyse sensor data using software to derive insights and perform anomaly detection and prediction based on an industry case study 

Course Fee

~ Non-MDTS Alumni   

~ MDTS Alumni           
   Free Admission for first 10 pax

Click here for more information of the course.