Internet of Things and Data Analytics for Industry
Date: 7 to 8 July 2021
Time: 9am to 5pm
Course Title : Internet of Things and Data Analytics for Industry Course
Dates : 7 & 8 July 2021 (Wednesday & Thursday) Time : 9.00am – 5.00pm
Instructor : Associate Prof. THAM Chen Khong, dept of ECE, NUS
Singapore Water Association will be conducting a 2 days Virtual training class on Internet of Things and Data Analytics for Industry on 7 & 8 July 2021. This programme is supported by Employment and Employability Institute Pte Ltd (“e2i”).
Course Objectives: 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.
• 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 use cases
2. IoT and Data Platforms
– Data collection, storage, query, analysis and applications (platform examples: ThingWorx, MindSphere, Azure)
3. Data Analytics for IoT Sensor Data
– Data analytics techniques: regression, clustering, and classification using logistic regression and support vector machine (SVM)
– Data modelling of time-series sensor data
– Anomaly detection from time-series sensor data, with applications in predictive maintenance
4. Hands-on Exercises
– Participants will analyse sensor data using the Python programming language which is widely used in industry. You will derive insights, detect anomalies and predict events. The necessary data sets and computer code will be provided to participants.
To confirm a place for this training, please REGISTER ONLINE by 25 June 2021.
Should you require more information or any assistance, please feel free to drop us an email at email@example.com or contact us at 65150812. Thank you for your continuing support and we look forward to your participation.
Instructor: Associate Professor Tham Chen Khong, Dept of ECE, NUS 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 IoT sensor data analytics, wireless mobile computing and machine learning algorithms and architectures. He has working experience 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.