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

Chapter 10. Data Science for IoT Analytics

"Revenues are up 5% due to your little geospatial search trick," the VP of Connected Services says, "You know your former boss's position is still open. Maybe we should fill it from the inside..."

Your pulse quickens, you were hoping he might come to this conclusion. You deserve a promotion after what your analytics has brought to the company. You now have one person working for you focusing on geospatial analysis. You can just imagine what you could do with a whole team.

"There is something that we have been toying around with though," he continues, "With all this data we are collecting, we should be able to tap into machine learning models to predict equipment failures. Some think we should be hiring an outside consulting company to handle all of it. Sounds expensive to me. I sure wish we could coordinate this ourselves, work with data scientists of our own choosing... know anyone that might be up to it?"

He winks and walks off, hands behind his...