Date : 12 May 2020 (Tuesday)

Time : 9:00am – 5:00pm 

Venue : National University of Singapore 

About the Instructor

Associate Professor Tham Chen Khong . Download his CV here.

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.

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 

For more information and registration, do send an email to (Ms Sim Kah Eng, Queenie)


Please refer here for more information