Developing tomorrow's industrial infrastructure is a significant challenge. This course goes beyond the hype of consumer IoT to emphasize a much greater space for potential embedded system applications and growth: The Industrial Internet of Things (IIoT), also known as Industry 4.0. Cisco’s CEO stated: “IoT overall is a $19 Trillion market. IIoT is a significant subset including digital oilfield, advanced manufacturing, power grid automation, and smart cities”. This is part 1 of the specialization. The primary objective of this specialization is to closely examine emerging markets, technology trends, applications and skills required by engineering students, or working engineers, exploring career opportunities in the IIoT space. The structure of the course is intentionally wide and shallow: We will cover many topics, but will not go extremely deep into any one topic area, thereby providing a broad overview of the immense landscape of IIoT. There is one exception: We will study security in some depth as this is the most important topic for all "Internet of Things" product development. In this course students will learn : * What Industry 4.0 is and what factors have enabled the IIoT * Key skills to develop to be employed in the IIoT space * What platforms are, and also market information on Software and Services * What the top application areas are (examples include manufacturing and oil &gas) * What the top operating systems are that are used in IIoT deployments * About networking and wireless communication protocols used in IIoT deployments * About computer security; encryption techniques and secure methods for insuring data integrity and authentication

This is part 2 of the specialization. In this course students will learn : * How to staff, plan and execute a project * How to build a bill of materials for a product * How to calibrate sensors and validate sensor measurements * How hard drives and solid state drives operate * How basic file systems operate, and types of file systems used to store big data * How machine learning algorithms work - a basic introduction * Why we want to study big data and how to prepare data for machine learning algorithms