This document discusses data science applications for Internet of Things (IoT) systems, specifically regarding air pollution monitoring. It introduces the presenter and provides an overview of topics like the data science life cycle in IoT, fog computing applications, and a case study on using IoT sensors and machine learning to monitor and predict particulate matter (PM2.5) air pollution levels in Thailand. The case study deployed IoT sensor nodes and mist sprayers to collect local weather and pollution data, which was analyzed using linear regression and support vector regression to better understand pollution trends and identify influential factors.