Enter the world of Internet of Things with the power of data science with this highly practical, engaging book About This Book * Explore real-world use cases from the Internet of Things (IoT) domain using decision science with this easy-to-follow, 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, example-rich guide fills the gap between your kwledge 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 n-technical 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 kw the end to end analytics stack - Descriptive + Inquisitive + Predictive + Prescriptive * Solve problems in IoT connected assets and connected operations * Design and solve real-life 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 easy-to-understand 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 kwledge with you onto whatever project calls for it Style and approach This scenario-based tutorial approaches the topic systematically, allowing you to build upon what you learned in previous chapters.
Jojo Moolayil is a data scientist, living in Bengaluru-the silicon valley of India. With over 4 years of industrial experience in Decision Science and IoT, he has worked with industry leaders on high impact and critical projects across multiple verticals. He is currently associated with GE, the pioneer and leader in data science for Industrial IoT. Jojo was born and raised in Pune, India and graduated from University of Pune with a major in information technology engineering. With a vision to solve problems at scale, Jojo found solace in decision science and learnt to solve a variety of problems across multiple industry verticals early in his career. He started his career with Mu Sigma Inc., the world's largest pure play analytics provider where he worked with the leaders of many fortune 50 clients. With the passion to solve increasingly complex problems, Jojo touch based with Internet of Things and found deep interest in the very promising area of consumer and industrial IoT. One of the early enthusiasts to venture into IoT analytics, Jojo converged his learnings from decision science to bring the problem solving frameworks and his learnings from data and decision science to IoT. To cement his foundations in industrial IoT and scale the impact of the problem solving experiments, he joined a fast growing IoT Analytics startup called Flutura based in Bangalore and headquartered in the valley. Flutura focuses exclusively on Industrial IoT and specializes in analytics for M2M data. It is with Flutura, where Jojo reinforced his problem solving skills for M2M and Industrial IoT while working for the world's leading manufacturing giant and lighting solutions providers. His quest for solving problems at scale brought the 'product' dimension in him naturally and soon he also ventured into developing data science products and platforms. After a short stint with Flutura, Jojo moved on to work with the leaders of Industrial IoT, that is, G.E. in Bangalore, where he focused on solving decision science problems for Industrial IoT use cases. As a part of his role in GE, Jojo also focuses on developing data science and decision science products and platforms for Industrial IoT.