Book description
Enter the world of Internet of Things with the power of data science with this highly practical, engaging book
About This Book
 Explore realworld use cases from the Internet of Things (IoT) domain using decision science with this easytofollow, practical book
 Learn to make smarter decisions on top of your IoT solutions so that your IoT is smart in a real sense
 This highly practical, examplerich guide fills the gap between your knowledge of data science and IoT
Who This Book Is For
If you have a basic programming experience with R and want to solve business use cases in IoT using decision science then this book is for you. Even if your're a nontechnical manager anchoring IoT projects, you can skip the code and still benefit from the book.
What You Will Learn
 Explore decision science with respect to IoT
 Get to know the end to end analytics stack ? Descriptive + Inquisitive + Predictive + Prescriptive
 Solve problems in IoT connected assets and connected operations
 Design and solve reallife IoT business use cases using cutting edge machine learning techniques
 Synthesize and assimilate results to form the perfect story for a business
 Master the art of problem solving when IoT meets decision science using a variety of statistical and machine learning techniques along with hands on tasks in R
In Detail
With an increasing number of devices getting connected to the Internet, massive amounts of data are being generated that can be used for analysis. This book helps you to understand Internet of Things in depth and decision science, and solve business use cases. With IoT, the frequency and impact of the problem is huge. Addressing a problem with such a huge impact requires a very structured approach.
The entire journey of addressing the problem by defining it, designing the solution, and executing it using decision science is articulated in this book through engaging and easytounderstand business use cases. You will get a detailed understanding of IoT, decision science, and the art of solving a business problem in IoT through decision science.
By the end of this book, you'll have an understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for it
Style and approach
This scenariobased tutorial approaches the topic systematically, allowing you to build upon what you learned in previous chapters.
Table of contents

Smarter Decisions – The Intersection of Internet of Things and Decision Science
 Smarter Decisions – The Intersection of Internet of Things and Decision Science
 Credits
 About the Author
 About the Reviewer
 eBooks, discount offers, and more
 Preface
 1. IoT and Decision Science

2. Studying the IoT Problem Universe and Designing a Use Case
 Connected assets & connected operations

Defining the business use case
 Defining the problem
 Researching and gathering context
 Prioritize and structure hypotheses based on the availability of data
 Validating and Improving the hypotheses (iterate over #2 and #3)
 Assimilate results and render the story
 Sensing the associated latent problems
 Designing the heuristic driven hypotheses matrix (HDH)
 Summary

3. The What and Why  Using Exploratory Decision Science for IoT
 Identifying gold mines in data for decision making
 Exploring each dimension of the IoT Ecosystem through data (Univariates)
 Studying relationships
 Exploratory data analysis
 Root Cause Analysis
 Summary

4. Experimenting Predictive Analytics for IoT
 Resurfacing the problem  What's next?
 Linear regression  predicting a continuous outcome
 Decision trees
 Logistic Regression  Predicting a categorical outcome
 Summary

5. Enhancing Predictive Analytics with Machine Learning for IoT
 A Brief Introduction to Machine Learning
 Ensemble modeling  random forest
 Ensemble modeling  XGBoost

Neural Networks and Deep Learning

So what is so cool about neural networks and deep learning?
 What is a neural network?
 So what is deep learning?
 So what problems can neural networks and deep learning solve?
 So how does a neural network work?
 Neurons
 Edges
 Activation function
 Learning
 So what are the different types of neural networks?
 How do we go about modeling using a neural network or deep learning technique?
 What next?
 What have we achieved till now?

So what is so cool about neural networks and deep learning?
 Packaging our results
 Summary

6. Fast track Decision Science with IoT
 Setting context for the problem
 Defining the problem and designing the approach
 Exploratory Data Analysis and Feature Engineering
 Building predictive model for the use case
 Packaging the solution
 Summary

7. Prescriptive Science and Decision Making
 Using a layered approach and test control methods to outlive business disasters
 Connecting the dots in the problem universe
 Story boarding  Making sense of the interconnected problems in the problem universe
 Implementing the solution
 Summary
 8. Disruptions in IoT
 9. A Promising Future with IoT
Product information
 Title: Smarter Decisions – The Intersection of Internet of Things and Decision Science
 Author(s):
 Release date: July 2016
 Publisher(s): Packt Publishing
 ISBN: 9781785884191
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