Designing Industrial IoT platforms requires integrating data processing, scalable architectures, and advanced analytics to support modern industrial operations. This course focuses on building robust IIoT platforms that enable efficient data handling and intelligent decision-making in connected environments.

You will learn to design and implement Industrial IoT architectures using tools such as Python, InfluxDB, Node-RED, Airflow, and Neo4j. The course guides you through integrating data pipelines, managing time-series data, and deploying analytics models to support real-world industrial applications. What sets this course apart is its combination of platform design and advanced analytics, including digital twin implementation and model deployment. This ensures you gain both technical depth and practical insight into building scalable, data-driven IIoT solutions. This course is ideal for developers, engineers, and IT professionals with prior knowledge of Industrial IoT or data systems. Familiarity with programming and basic data concepts will be beneficial. This course is part two of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.