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Smarter Decisions - The Intersection of Internet of Things and Decision Science

You're reading from   Smarter Decisions - The Intersection of Internet of Things and Decision Science A comprehensive guide for solving IoT business problems using decision science

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781785884191
Length 392 pages
Edition 1st Edition
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Author (1):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
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Table of Contents (10) Chapters Close

Preface 1. IoT and Decision Science FREE CHAPTER 2. Studying the IoT Problem Universe and Designing a Use Case 3. The What and Why - Using Exploratory Decision Science for IoT 4. Experimenting Predictive Analytics for IoT 5. Enhancing Predictive Analytics with Machine Learning for IoT 6. Fast track Decision Science with IoT 7. Prescriptive Science and Decision Making 8. Disruptions in IoT 9. A Promising Future with IoT

Exploring each dimension of the IoT Ecosystem through data (Univariates)

Let's dig deeper into each dimension in the IoT use case to understand more realistically what the data showcases. We will perform extensive univariate analysis to study and visualize the entire data landscape.

What does the data say?

We visited the data dimensions while exploring the gold mines in data (in the previous section) and understood that Product_Qty_Unit, Product_ID, Material_ID, and Product_Name indicate that the columns contain a single value. Therefore, we conclude that the data in the use case is provided for a specific product and its output is measured in Kgs. Let's start exploring Order Quantity and Produced Quantity in depth. We initially studied the data dimensions using summary commands that gave us the percentile distribution. Let's take this one step further.

Order Quantity and Produced Quantity are both continuous variables, that is, a variable that can have infinite number of values...

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