This lecture delves into the techniques and tools used to transform vast amounts of raw IoT data into valuable information, actionable insights, and predictive wisdom, focusing on the critical role of data analytics and visualization in the IoT ecosystem. Time-series data, the dominant data type in IoT characterized by high volume, velocity, and the need for specialized databases like InfluxDB or Prometheus, is learned about. The importance of data pre-processing, including filtering, smoothing, and outlier detection, to clean raw sensor data is discovered. Key visualization tools like Grafana for real-time monitoring, Microsoft Power BI for business intelligence, and Node-RED for rapid prototyping of web-based UIs are explored. The role of Machine Learning in IoT, from supervised learning for predictive maintenance to unsupervised learning for anomaly detection and reinforcement learning for HVAC optimization, is understood. Data analytics and visualization are recognized as the culmination of the IoT process, where the deployment of hardware and management of networks translate into tangible value, with Machine Learning marking a shift from hindsight to foresight and enabling systems that predict failures and optimize their behavior.