Using Sensors With Your Raspberry Pi from Coursera

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.

What's inside

Syllabus

Designing Sensors

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 move into the concepts of accuracy, precision, and uncertainty, which are necessary for understanding the inherent error in any measurement system. This module lays the groundwork for the circuits and examples in later modules.

Calibration Methods

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 you can implement in your Raspberry Pi projects.

Interface Circuits

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.

Introduction to Signal Processing

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 up your sensor data.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Useful for individuals with an interest in hardware and electronics

Specialization is available for computer science students

Builds on concepts from two previous Specialization courses

Geared towards learners with a background in Python

Requires access to a Raspberry Pi or similar platform

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Using Sensors With Your Raspberry Pi with these activities:

Attend meetups or conferences on sensor technology

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Provides opportunities to connect with experts in the field, learn about the latest trends, and stay updated on advancements.

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Identify meetups or conferences related to sensor technology.
Attend the events and participate in discussions.
Connect with speakers, attendees, and industry professionals.

Read Getting Started with Raspberry Pi Sensors

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Provides a comprehensive overview of Raspberry Pi sensors and how to use them.

View Getting Started With Raspberry Pi: Getting to... on Amazon

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Read through the book's chapters on sensor basics, interfacing sensors with the Raspberry Pi, and using sensors for data collection.
Complete the hands-on exercises in the book to reinforce your understanding of the material.

Practice data processing and visualization with Python

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Strengthens your ability to manipulate and visualize data, which is crucial for working with sensor data.

Browse courses on Python

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Use Python to read data from a sensor.
Clean and process the data using Python.
Visualize the data using Python libraries such as matplotlib and seaborn.

Five other activities

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Explore the Raspberry Pi's built-in sensors

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Develops your understanding of Raspberry Pi sensors by exploring the different sensors directly on your Raspberry Pi.

Browse courses on Raspberry Pi

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Follow tutorials to set up and use the Raspberry Pi's temperature sensor.
Use the Raspberry Pi's camera module to capture images and videos.
Connect an external sensor to your Raspberry Pi and read its data.

Simulate sensor data pipeline

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Simulate a simple sensor data pipeline to demonstrate how to structure your project and apply your knowledge.

Browse courses on Python

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Start with a simple sensor model that generates realistic simulated sensor readings.
Add noise to the simulated data to make it more realistic.
Process the simulated data using the techniques you learned in the course.
Visualize the results of your processing.

Build a sensor-based project from scratch

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Builds on and tests your knowledge by allowing you to apply what you've learned in the course to a real-world scenario.

Browse courses on Hardware

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Identify a problem that can be solved using a sensor-based system.
Design and build the hardware for your sensor-based system.
Write the software to process the data from your sensor and control your system.
Test and evaluate your sensor-based system.

Summarize and integrate notes and materials

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Aids in retention and comprehension by encouraging the organization and integration of learning materials.

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Review and summarize notes from the course modules.
Organize and integrate notes with additional materials such as handouts and articles.
Create a study guide or cheat sheet for easy reference.

Contribute to open-source sensor projects

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Fosters collaboration, deepens understanding, and allows you to apply your knowledge to real-world projects.

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Find open-source sensor projects on platforms like GitHub.
Review the project documentation and code.
Identify an area where you can contribute, such as adding a new feature or fixing a bug.
Submit a pull request with your changes.

Career center

Learners who complete Using Sensors With Your Raspberry Pi will develop knowledge and skills that may be useful to these careers:

Sensor Engineer

A Sensor Engineer designs, develops, and tests sensors. They work in a variety of industries, including automotive, aerospace, and medical devices. This course provides a strong foundation in sensor engineering principles, such as sensor types, signal conditioning, and data acquisition.

See salaries and explore the career path for Sensor Engineer

Electronics Engineer

An Electronics Engineer designs, develops, tests, and manufactures electronic devices and systems. They work in a variety of industries, including consumer electronics, medical devices, and telecommunications. This course may be useful in providing a foundation in electronics engineering principles, such as digital logic, analog circuits, and embedded systems.

See salaries and explore the career path for Electronics Engineer

Mechanical Engineer

A Mechanical Engineer designs, develops, and tests mechanical systems. They work in a variety of industries, including automotive, aerospace, and manufacturing. This course may be useful in providing a foundation in mechanical engineering principles, such as mechanics of materials, thermodynamics, and fluid dynamics.

See salaries and explore the career path for Mechanical Engineer

Electrical Engineer

An Electrical Engineer designs, develops, tests, and supervises the installation of electrical systems. They work in a variety of industries, including automotive, aerospace, manufacturing, and telecommunications. This course may be useful in providing a foundation in electrical engineering principles, such as circuit analysis, digital signal processing, and control systems.

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Robotics Engineer

A Robotics Engineer designs, develops, and tests robots. They work in a variety of industries, including manufacturing, healthcare, and defense. This course may be useful in providing a foundation in robotics engineering principles, such as kinematics, dynamics, and control systems.

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Mechatronics Engineer

A Mechatronics Engineer designs, develops, and maintains systems that integrate mechanical, electrical, and computer engineering principles. They work in a variety of industries, including robotics, automation, and manufacturing. This course may be useful in providing a foundation in mechatronics engineering principles, such as control systems, sensors, and actuators.

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Manufacturing Engineer

A Manufacturing Engineer plans, designs, and oversees the production of goods. They work in a variety of industries, including automotive, aerospace, and medical devices. This course may be useful in providing a foundation in manufacturing engineering principles, such as process planning, quality control, and materials science.

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Quality Engineer

A Quality Engineer develops and implements quality assurance and quality control procedures to ensure that products meet customer requirements. They work in a variety of industries, including manufacturing, software, and healthcare. This course may be useful in providing a foundation in quality engineering principles, such as quality management, statistical process control, and risk management.

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Industrial Engineer

An Industrial Engineer designs, improves, and installs integrated systems of people, materials, and equipment. They work in a variety of industries, including manufacturing, healthcare, and logistics. This course may be useful in providing a foundation in industrial engineering principles, such as process improvement, systems analysis, and data analysis.

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Data Scientist

A Data Scientist is a professional who uses their knowledge of data analysis, statistics, and computer science to solve business problems. They develop and implement data-driven solutions to improve business outcomes. This course may be helpful in providing a foundation in data analysis and data processing techniques, which can be applied in a variety of industries.

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Data Analyst

A Data Analyst is a business professional who analyzes data to uncover trends, patterns, and insights. They use their findings to make recommendations to businesses on how to improve their operations, marketing, and customer service. This course may be useful in providing a foundation in data analysis techniques, such as statistical analysis, data visualization, and data mining, which can be applied in a variety of industries.

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Product Manager

A Product Manager is responsible for the development, launch, and marketing of a product. They work closely with engineering, design, and marketing teams to ensure that the product meets customer needs. This course may be useful in providing a foundation in product management principles, such as market research, product planning, and customer feedback.

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Systems Engineer

A Systems Engineer designs, develops, and tests complex systems. They work in a variety of industries, including aerospace, defense, and telecommunications. This course may be useful in providing a foundation in systems engineering principles, such as system architecture, requirements analysis, and risk management.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. They work in a variety of industries, including technology, finance, and healthcare. This course may be useful in providing a foundation in software engineering principles, such as object-oriented programming, data structures, and algorithms.

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Test Engineer

A Test Engineer designs, develops, and executes tests to ensure that products meet specifications. They work in a variety of industries, including manufacturing, software, and healthcare. This course may be useful in providing a foundation in test engineering principles, such as test planning, test execution, and data analysis.

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