What Every Engineer Should Know About Data Driven Analytics

What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the theoretical concepts and approaches of machine learning that are used in predictive data analytics. By introducing the theory and by providing practical applications, this text can be understood by every engineering discipline. It offers a detailed and focused treatment of the important machine learning approaches and concepts that can be exploited to build models to enable decision making in different domains. Utilizes practical examples from different disciplines and sectors within engineering and other related technical areas to demonstrate how to go from data, to insight, and to decision making Introduces various approaches to build models that exploits different algorithms Discusses predictive models that can be built through machine learning and used to mine patterns from large datasets Explores the augmentation of technical and mathematical materials with explanatory worked examples Includes a glossary, self-assessments, and worked-out practice exercises Written to be accessible to non-experts in the subject, this comprehensive introductory text is suitable for students, professionals, and researchers in engineering and data science.

What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the theoretical concepts and approaches of machine learning that are used in predictive data analytics. By introducing the theory and by providing practical applications, this text can be understood by every engineering discipline. It offers a detailed and focused treatment of the important machine learning approaches and concepts that can be exploited to build models to enable decision making in different domains. Utilizes practical examples from different disciplines and sectors within engineering and other related technical areas to demonstrate how to go from data, to insight, and to decision making Introduces various approaches to build models that exploits different algorithms Discusses predictive models that can be built through machine learning and used to mine patterns from large datasets Explores the augmentation of technical and mathematical materials with explanatory worked examples Includes a glossary, self-assessments, and worked-out practice exercises Written to be accessible to non-experts in the subject, this comprehensive introductory text is suitable for students, professionals, and researchers in engineering and data science.

Accessibility is a core quality of digital products to be deliberately addressed throughout the development lifecycle. What Every Engineer Should Know About Digital Accessibility will prepare readers to integrate digital accessibility into their engineering practices. Readers will learn how to accurately frame accessibility as an engineering challenge so they are able to address the correct problems in the correct way. Illustrated with diverse perspectives from accessibility practitioners and advocates, this book describes how people with disabilities use technology, the nature of accessibility barriers in the digital world, and the role of engineers in breaking down those barriers. Accessibility competence for current, emerging, and future technologies is addressed through a combination of guiding principles, core attributes and requirements, and accessibility‐informed engineering practices. FEATURES Discusses how technology can support inclusion for people with disabilities and how rigorous engineering processes help create quality user experiences without introducing accessibility barriers Explains foundational principles and guidelines that build core competency in digital accessibility as they are applied across diverse and emerging technology platforms Highlights practical insights into how engineering teams can effectively address accessibility throughout the technology development lifecycle Uses international standards to define and measure accessibility quality Written to be accessible to non‐experts in the subject area, What Every Engineer Should Know About Digital Accessibility is aimed at students, professionals, and researchers in the field of software engineering.

Get ready to be at the forefront of the future of urban development! As cities continue to rapidly grow, the demand for sustainable and efficient infrastructure becomes more urgent. That’s where What Every Engineer Should Know About Smart Cities comes in, offering a comprehensive guide to the concepts and technologies driving the transformation of our cities. Delve into the world of smart cities and discover how information and communication technologies are revolutionizing urban environments. With clear definitions and a focus on real-world applications, this book explores the benefits and challenges of smart cities. It also highlights interdisciplinary topics such as smart buildings, autonomous cars, and urban emergency management systems. This book is not just a theoretical exploration of smart cities. It goes beyond that by providing an in-depth look at the key technologies that are essential to creating smart cities. From the Internet of Things and blockchain to digital twins and modeling and simulations, readers will gain a solid understanding of the foundational technologies that make smart cities possible. With detailed discussions and real-world examples of smart mobility, smart health, smart education, and smart agribusiness, readers will gain a deep understanding of the requirements and characteristics that engineers need to contribute to the development of smart cities. Whether you’re an engineer looking to expand your knowledge, a city planner seeking to understand the latest trends, or simply someone interested in the future of urban living, What Every Engineer Should Know About Smart Cities is the ultimate guide to unlocking the potential of smart cities for sustainable urban development and improved quality of life.

Emphasises an introduction and explanation of the practical methods used in reliability, and risk studies with a discussion of their uses and limitations Offers basic and advanced methods in reliability analysis that are commonly used in daily practice Provides methods that address unique topics such as dependent failure analysis, importance analysis, and analysis of repairable systems Presents a comprehensive overview of modern probabilistic life assessment methods such as Bayesian estimation, system reliability analysis, and human reliability Includes many ends of chapter problems, a tools website with computational codes, along with a solutions manual to support course adoptions

Gain a competitive edge in today's data-driven world and build a rich career as a data professional that drives business success and innovation... Did you know that every minute, people around the world make 5.9 million searches on Google, share 1.7 million posts on Facebook, and watch 1 million hours of videos? And that's not even all of it! In total, the world creates a massive 328 million terabytes of data every day! Data is everywhere... and it has become the essential building block of this modern society, shaping the future of businesses, technology, and human interactions. It's no wonder that data professional roles, such as data analysts, data scientists, and data architects are now highly in demand in this data-driven world. And that's why now is the perfect time to pursue a career in data. But what does it take to become a competent data professional? This book has all the answers! Building a successful career in data is all about cultivating the necessary mindset, knowledge, and skills. This book is your ultimate guide to understanding the fundamentals of data analytics, helping you unlock the expertise of efficiently solving real-world data-related problems. Here is just a fraction of what you will discover: A comprehensive guide to the data analytics process - a beginner-friendly 5-step framework to kickstart your journey into analyzing and processing data How to get started with the fundamental concepts, theories, and models for accurately analyzing data Everything you ever needed to know about data mining and machine learning principles - your gateway to unlocking the secrets of data's hidden potential Why business run on a data-driven culture, and how you can leverage it using real-time business intelligence analytics Strategies and techniques to build a problem-solving mindset that can overcome any complex and unique dataset How to create compelling and dynamic visualizations that help generate insights and make data-driven decisions The 4 pillars of a new digital world - discover how emerging technologies will transform the landscape of analyzing data And much more. Believe it or not, you can be terrible in math or statistics and still pursue a career in data. Yes, you need a good grasp of the basics but always keep in mind that knowledge can be acquired and skills can be developed. The key is to not be intimidated by the strings of characters and numbers. Focus, instead, on building a mindset that thirsts for knowledge. You'll be surprised by how much you're able to do with just that. And this book is here to guide you throughout this journey, so that crunching data becomes second nature to you. So, what are you waiting for? Ready to master the fundamentals and build a successful career in data analytics?

This book introduces condition-based maintenance (CBM)/data-driven prognostics and health management (PHM) in detail, first explaining the PHM design approach from a systems engineering perspective, then summarizing and elaborating on the data-driven methodology for feature construction, as well as feature-based fault diagnosis and prognosis. The book includes a wealth of illustrations and tables to help explain the algorithms, as well as practical examples showing how to use this tool to solve situations for which analytic solutions are poorly suited. It equips readers to apply the concepts discussed in order to analyze and solve a variety of problems in PHM system design, feature construction, fault diagnosis and prognosis.

DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.