The Internet of Things and decision science are among the most trending topics in the industry right now. The problems we solve today have become increasingly ambiguous, uncertain and volatile, and therefore the means to solve them. Moreover, problem solving has evolved from solving one specific problem using data science to the art of problem solving using decision science. The Internet of Things provides a massive opportunity for business to make human life easier which can only be leveraged using decision science. Smarter Decisions – The Intersection of Internet of Things and Decision Science, will help you learn the nuances of IoT and Decision and practically aid you in smarter decision making by solving real-life Industrial & Consumer IoT use cases. The book gives paramount focus on solving a fundamental problem. Therefore, the entire journey of addressing the problem by defining, designing and executing it using industry standard frameworks for decision science is articulated through engaging and easy-to-understand business use cases. While solving the business use cases, we will touch base with the entire data science stack that is descriptive + inquisitive + predictive + prescriptive analytics by leveraging the most popular and open source software 'R'. By the end of this book, you'll have complete 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.
Chapter 1, IoT and Decision Science, briefly introduces the two most important topics for the book in the most lucid way using intuitive real-life examples. The chapter briefs about IoT, its evolution and the key differences between IoT, IIoT, Industrial Internet, Internet of Everything. Decision science is narrated by providing paramount focus on the problem and its evolution in the universe. Finally we explore the problem solving framework to study the decision science approach for problem solving.
Chapter 2, Studying the IoT Problem Universe and Designing a Use Case, introduces a real life IoT business problem and aids the reader to practically design the solution for the problem by using a structured and mature problem solving framework learnt in the preceding chapter. The chapter also introduces the two main domains in IoT that is connected assets and connected operations and various artefacts and thought leadership frameworks that will be leveraged to define and design a solution for the business problem.
Chapter 3, The What and the Why – Using Exploratory Decision Science for IoT, focuses on practically solving the IoT business use case designed in the preceding chapter using the R software for exploratory data analysis. Leveraging an anonymized and masked dataset for the business use case along with the hands on exercises aids the reader to practically traverse through the descriptive and inquisitive phases of decision science. The problem's solution is addressed by answering the two fundamental questions What and Why by performing univariate, bivariate analyses along with various statistical tests to validate the results and thereby render the story.
Chapter 4, Experimenting Predictive Analytics for IoT, enhances the solution of the business use case by leveraging predictive analytics. In this chapter, we answer the question "when" to solve the problem with more clarity. Various statistical models like linear regression, logistic regression and decision trees are explored to solve the different predictive problems that were surfaced during the inquisitive phase of the business use case in the preceding chapter. Intuitive examples to understand the mathematical functioning of the algorithms and easy means to interpret the results are articulated to cement the foundations of predictive analytics for IoT.
Chapter 5, Enhancing Predictive Analytics with Machine Learning for IoT, takes an attempt to improve the results of predictive modelling exercises in the preceding chapter by leveraging cutting edge machine learning algorithms like Random Forest, XgBoost and deep learning algorithms like multilayer perceptrons. With improved results from improved algorithms, the solution for the use case is finally completed by leveraging the 3 different layers of decision science: descriptive + inquisitive + predictive analytics.
Chapter 6, Fast track Decision Science with IoT, reinforces the problem solving skills learnt so far by attempting to solve another fresh IoT use case from start to end within the same chapter. The entire journey of defining, designing and solving the IoT problem is articulated in a fast track mode.
Chapter 7, Prescriptive Science and Decision Making, introduces the last layer of the decision science stack i.e. prescriptive analytics by leveraging a hypothetical use case. The entire journey of evolution of a problem from descriptive to inquisitive to predictive and finally to prescriptive and back is illustrated with simple and easy to learn examples. After traversing the problem through prescriptive analytics, the art of decision making and storyboarding to convey the results in the most lucid format is explored in detail.
Chapter 8, Disruptions in IoT, explores the current disruptions in IoT by studying a few like fog computing, cognitive computing, Next generation robotics and genomics and autonomous cars. Finally the privacy and security aspects in IoT is also explored in brief.
Chapter 9, A Promising Future with IoT, discusses about how the near future will radically change human life with the unprecedented growth of IoT. The chapter explores the visionary topics of the new IoT business models such as, AssetDevice as a service and the evolution of connected cars to smart cars & connected humans to smart humans.
In order to make your learning efficient, you need to have a computer with either Windows, Mac, or Ubuntu.
You need to download and install R to execute the codes mentioned in this book. You can download and install R using the CRAN website available at http://cran.r-project.org/. All the codes are written using RStudio. RStudio is an integrated development environment for R and can be downloaded from http://www.rstudio.com/products/rstudio/.
The different R packages used in the book are freely available to download and install for all operating systems mentioned above.
Smarter Decisions – The intersection of Internet of Things and Decision Science is intended for data science and IoT enthusiasts or project managers anchoring IoT Analytics projects. Basic knowledge of R in terms of its libraries is an added advantage, however the verbiage for interpretation of the results will be independent of the codes. Any non-technical data science and IoT enthusiast can skip the codes and read through the output and still be able to consume the results.
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