Book Image

Analytics for the Internet of Things (IoT)

By : Andrew Minteer
5 (1)
Book Image

Analytics for the Internet of Things (IoT)

5 (1)
By: Andrew Minteer

Overview of this book

We start with the perplexing task of extracting value from huge amounts of barely intelligible data. The data takes a convoluted route just to be on the servers for analysis, but insights can emerge through visualization and statistical modeling techniques. You will learn to extract value from IoT big data using multiple analytic techniques. Next we review how IoT devices generate data and how the information travels over networks. You’ll get to know strategies to collect and store the data to optimize the potential for analytics, and strategies to handle data quality concerns. Cloud resources are a great match for IoT analytics, so Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail next. Geospatial analytics is then introduced as a way to leverage location information. Combining IoT data with environmental data is also discussed as a way to enhance predictive capability. We’ll also review the economics of IoT analytics and you’ll discover ways to optimize business value. By the end of the book, you’ll know how to handle scale for both data storage and analytics, how Apache Spark can be leveraged to handle scalability, and how R and Python can be used for analytic modeling.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Machine learning (ML)


As a wise uncle of a human-arachnid hybrid once said, With great power, comes great responsibility. This is very true of ML. There are many ways to go wrong. When in the hands of a skilled practitioner, it truly is a form of art. It can be used to do some incredible things on a grand scale, but it should come with a big caution sign. Use it carefully. Be paranoid and validate, validate, validate.

Although we will be going over some core concepts and providing code that you can take and run yourself, this is a big field with lots to learn. It takes years to skillfully and competently apply it. Each section in this chapter is really a book in itself. No, many books. If you plan to use it yourself on IoT data, read, read, read, and then read some more. This chapter is meant to able to provide you with a good foundation to have meaningful conversations with data scientists on the subject.

What is machine learning?

Ask a hundred experts for the definition of ML, and you are...