One-Day Course 22 May 2020 – Online Learning Via Zoom
About the Instructor
Associate Professor Tham Chen Khong http://www.ece.nus.edu.sg/stfpage/eletck/
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.
~ 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
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
~ Non-MDTS Alumni
~ MDTS Alumni
Free Admission for first 10 pax
Click here for more information of the course.