Internet of Things and Data Analytics for Industry

Internet of Things and Data Analytics for Industry

Date: 26 - 27 April 2021
Time: 9am to 5pm
Venue: PUB WaterHub

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.

Learning Outcomes
• 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

Course Topics
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.

E2i course fee subsidy*
? 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

Should there be any further information or assistance required, please feel free to email