This course on integrating sensors with your Raspberry Pi is course 3 of a Coursera Specialization and can be taken separately or as part of the specialization. Although some material and explanations from the prior two courses are used, this course largely assumes no prior experience with sensors or data processing other than ideas about your own projects and an interest in building projects with sensors.
This course focuses on core concepts and techniques in designing and integrating any sensor, rather than overly specific examples to copy. This method allows you to use these concepts in your projects to build highly customized sensors for your applications.
Some of the ideas covered include calibrating sensors and the trade-offs between different mathematical methods of storing and applying calibration curves to your sensors. We also discuss accuracy, precision, and how to understand uncertainty in your measurements. We study methods of interfacing analog sensors with your Raspberry Pi (or other platform) with amplifiers and the theory and technique involved in reducing noise with spectral filters. Lastly, we borrow from the fields of data science, statistics, and digital signal processing, to post-process our data in Python.
This first module gets us all on the same page, no matter how much experience you have with sensors or measurement technology. We'll start by describing a straightforward sensor flow model to help us understand the myriad of sensors available in the world, and which you may later build. Then we'll…
In this module, we'll look at examples of three common methods to store calibration data and apply that data to your sensor measurements. These examples range from simple to sophisticated, but none are complicated. We'll use Python and advanced open-source libraries to do the heavy math, just like…
Once you have a sensor, and have a Raspberry Pi, there is often a need for circuitry in the middle to interface the two. In this module, we'll show how simple amplifier and filter circuits can be used to adapt voltage levels and reduce noise from your sensor data.
The great thing about using a Raspberry Pi for your sensor projects is that you have access to great open-source software libraries and lots of processing power to manipulate your sensor data. This module looks at a few techniques for using statistical and digital signal processing methods to clean…
Some points apply to every course of this kind; see how we rank.
Johns Hopkins University is in Tier 1: world-leading universities and India's top institutes of our institution ranking (93/100).
An introduction to cybersecurity for technical and non-technical audiences alike. Learn how to secure your accounts, data, systems, and software against today’s threats and how to recognize and evaluate tomorrow’s as well, both at home and at work. Learn how to preserve your own privacy. Learn to v…
Harvard University · edX
Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
Harvard University · edX
Learn the concepts and techniques that make up the foundation of data science and machine learning.
Harvard University · edX
An introduction to the intellectual enterprises of computer science and the art of programming.
Instructors: Esther Duflo and Sara Ellison View the complete course: https://ocw.mit.edu/courses/14-310x-data-analysis-for-social-scientists-spring-2023 This course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. We will start with esse…
Harvard University · edX
Build a foundation in R and learn how to wrangle, analyze, and visualize data.
