Developing Industrial Internet of Things

Offered By: University of Colorado Boulder via Coursera

Course Description

Overview

The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.

Syllabus

Course 1: Industrial IoT Markets and Security
- Offered by University of Colorado Boulder. This course can also be taken for academic credit as ECEA 5385, part of CU Boulder’s Master of ... Enroll for free.

Course 2: Project Planning and Machine Learning
- Offered by University of Colorado Boulder. This course can also be taken for academic credit as ECEA 5386, part of CU Boulder’s Master of ... Enroll for free.

Course 3: Modeling and Debugging Embedded Systems
- Offered by University of Colorado Boulder. This course can also be taken for academic credit as ECEA 5387, part of CU Boulder’s Master of ... Enroll for free.

Courses

0 reviews
21 hours 33 minutes
View details

This course can also be taken for academic credit as ECEA 5385, part of CU Boulder’s Master of Science in Electrical Engineering degree. 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
0 reviews
17 hours 27 minutes
View details

This course can also be taken for academic credit as ECEA 5386, part of CU Boulder’s Master of Science in Electrical Engineering degree. 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
0 reviews
7 hours 51 minutes
View details

This course can also be taken for academic credit as ECEA 5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is part 3 of the specialization. In this course students will learn : * About SystemC and how it can be used to create models of cyber-physical systems in order to perform "what-if" scenarios * About Trimble Engineering's embedded systems for heavy equipment automation * A deeper understanding of embedded systems in the Automotive and Transportation market segment * How to debug deeply embedded systems * About Lauterbach's TRACE32 debugging tools * How to promote technical ideas within a company * What can be learned from studying engineering failures

Taught by

David Sluiter