Implement real-time data processing applications on the Raspberry Pi. This book uniquely helps you work with data science concepts as part of real-time applications using the Raspberry Pi as a localized cloud.  

You’ll start with a brief introduction to data science followed by a dedicated look at the fundamental concepts of Python programming. Here you’ll install the software needed for Python programming on the Pi, and then review the various data types and modules available. The next steps are to set up your Pis for gathering real-time data and incorporate the basic operations of data science related to real-time applications. You’ll then combine all these new skills to work with machine learning concepts that will enable your Raspberry Pi to learn from the data it gathers. Case studies round out the book to give you an idea of the range of domains where these concepts can be applied. 

By the end of Data Science with the Raspberry Pi, you’ll understand that many applications are now dependent upon cloud computing. As Raspberry Pis are cheap, it is easy to use a number of them closer to the sensors gathering the data and restrict the analytics closer to the edge. You’ll find that not only is the Pi an easy entry point to data science, it also provides an elegant solution to cloud computing limitations through localized deployment.

What You Will Learn

  • Interface the Raspberry Pi with sensors
  • Set up the Raspberry Pi as a localized cloud
  • Tackle data science concepts with Python on the Pi

    Who This Book Is For

    Data scientists who are looking to implement real-time applications using the Raspberry Pi as an edge device and localized cloud. Readers should have a basic knowledge in mathematics, computers, and statistics. A working knowledge of Python and the Raspberry Pi is an added advantage.

    Table of Contents

    1. Cover
    2. Front Matter
    3. 1. Introduction to Data Science
    4. 2. Basics of Python Programming
    5. 3. Introduction to the Raspberry Pi
    6. 4. Sensors and Signals
    7. 5. Preparing the Data
    8. 6. Visualizing the Data
    9. 7. Analyzing the Data
    10. 8. Learning from Data
    11. 9. Case Studies
    12. Back Matter