The Internet of Things (IoT) stands to be the next revolution in computing. Billions of data-spouting devices connected to the Internet are already fundamentally changing the way we live and work. This course teaches a deep understanding of IoT technologies from the ground up. Students will learn IoT device programming (Arduino and Raspberry Pi), sensing and actuating technologies, IoT protocol stacks (Zigbee, 5G, NFC, MQTT, etc), networking backhaul design and security enforcement, data science for IoT, and cloud-based IoT platforms such as AWS IoT. Students will be guided through laboratory assignments designed to give them practical real-world experience, where they will deploy a distributed wifi monitoring service, a cloud-based IoT service platform serving tens of thousands of heartbeat sensors, and more. Students will emerge from the class with a cutting-edge education on this rapidly emerging technology segment, and with the confidence to carry out tasks they will commonly encounter in industrial settings. Important: This whole series of courses consists in part of practical experimentation using actual hardware, which you will need to acquire. (Cost may vary between 100 and 200 USD depending on your location). Most parts that are needed for the first course, will be re-used in the following courses.
Internet of Things (IoT) is an emerging area of information and communications technology (ICT) involving many disciplines of computer science and engineering including sensors/actuators, communications networking, server platforms, data analytics and smart applications. IoT is considered to be an essential part of the 4th Industrial Revolution along with AI and Big Data. This course will be very useful to senior undergraduate and graduate students as well as engineers who are working in the industry. This course aims at introducing the general concepts and architecture of IoT applications, networking technologies involved, IoT development kits including Arduino, Raspberry Pi, Samsung ARTIK, and how to program them. This course will be offered in English. Subtitles/captions in both of English and Korean will be also provided.
There are billions of devices in homes, factories, oil wells, hospitals, cars, and thousands of other places. With the proliferation of devices, you increasingly need solutions to connect them, and collect, store, and analyze device data. AWS IoT provides broad and deep functionality, spanning the edge to the cloud, so you can build IoT solutions for virtually any use case across a wide range of devices.
This course will introduce you to the Internet of Things and then explore Amazon Web Services’ IoT services, and then expert instructors will dive deep into topics such as the device gateway, device management, the device registry, and shadows. They will also discuss security features and implications, core and edge computing capabilities and benefits, and the use of HTTP and MQTT as communications protocols. Lastly, they will discuss the integration of IoT solutions with analytics tools, which will allow you to analyze the IoT data being collected by your fleet of devices.
This course will provide a combination of video-based lectures, demonstrations and hands-on lab exercises, run in your own AWS account, that will allow you to build, deploy and manage your own IoT solution.
The explosive growth of the “Internet of Things” is changing our world and the rapid drop in price for typical IoT components is allowing people to innovate new designs and products at home. In this first class in the specialization you will learn the importance of IoT in society, the current components of typical IoT devices and trends for the future. IoT design considerations, constraints and interfacing between the physical world and your device will also be covered. You will also learn how to make design trade-offs between hardware and software. We'll also cover key components of networking to ensure that students understand how to connect their device to the Internet. Please note that this course does not include discussion forums.
Upon completing this course, you will be able to:
1. Define the term “Internet of Things”
2. State the technological trends which have led to IoT
3. Describe the impact of IoT on society
4. Define what an embedded system is in terms of its interface
5. Enumerate and describe the components of an embedded system
6. Describe the interactions of embedded systems with the physical world
7. Name the core hardware components most commonly used in IoT devices
8. Describe the interaction between software and hardware in an IoT device
9. Describe the role of an operating system to support software in an IoT device
10. Explain the use of networking and basic networking hardware
11. Describe the structure of the Internet
12. Describe the meaning of a “network protocol”
13. Explain MANETs and their relation to IoT
In this course, learners will be introduced to the concept of the Industrial Internet of Things, or IIoT, learn how it is applied in manufacturing, and what businesses should consider as they decide to implement this technology. Considerations include information technology infrastructure, the business value of implementing IIoT, and what needs to happen across the organization to ensure successful implementation.
Learners will hear from industry experts as they share their perspectives on the opportunities and challenges of implementing IIoT, how IIoT is being implemented in their companies, and insights on the future of this technology within their industry and across manufacturing.
The content presented in this course draws on a number of real-life interviews and case studies, and was created through a partnership with Siemens.
What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches?
This course provides a framework to analyze these concerns as you examine the ethical and privacy implications of collecting and managing big data. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency as you gain a deeper understanding of the importance of a shared set of ethical values. You will examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems while also learning best practices for responsible data management, understanding the significance of the Fair Information Practices Principles Act and the laws concerning the "right to be forgotten."
This course will help you answer questions such as who owns data, how do we value privacy, how to receive informed consent and what it means to be fair.
Data scientists and anyone beginning to use or expand their use of data will benefit from this course. No particular previous knowledge needed.
The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements. Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data. Predicting future trends and behaviors allows for proactive, data-driven decisions. During the class learners will acquire new skills to apply predictive algorithms to real data, evaluate, validate and interpret the results without any pre requisites for any kind of programming. Participants will gain the essential skills to design, build, verify and test predictive models.
You Will Learn
• How to design Data Science workflows without any programming involved
• Essential Data Science skills to design, build, test and evaluate predictive models
• Data Manipulation, preparation and Classification and clustering methods
• Ways to apply Data Science algorithms to real data and evaluate and interpret the results
People analytics is a data-driven approach to managing people at work. For the first time in history, business leaders can make decisions about their people based on deep analysis of data rather than the traditional methods of personal relationships, decision making based on experience, and risk avoidance. In this brand new course, three of Wharton’s top professors, all pioneers in the field of people analytics, will explore the state-of-the-art techniques used to recruit and retain great people, and demonstrate how these techniques are used at cutting-edge companies. They’ll explain how data and sophisticated analysis is brought to bear on people-related issues, such as recruiting, performance evaluation, leadership, hiring and promotion, job design, compensation, and collaboration. This course is an introduction to the theory of people analytics, and is not intended to prepare learners to perform complex talent management data analysis. By the end of this course, you’ll understand how and when hard data is used to make soft-skill decisions about hiring and talent development, so that you can position yourself as a strategic partner in your company’s talent management decisions. This course is intended to introduced you to Organizations flourish when the people who work in them flourish. Analytics can help make both happen. This course in People Analytics is designed to help you flourish in your career, too.