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Author: Peter Prevos
Publisher: CRC Press
ISBN: 100085647X
Category : Computers
Languages : en
Pages : 212
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Book Description
This addition to the Data Science Series introduces the principles of data science and the R language to the singular needs of water professionals. The book provides unique data and examples relevant to managing water utility and is sourced from the author’s extensive experience. Data Science for Water Utilities: Data as a Source of Value is an applied, practical guide that shows water professionals how to use data science to solve urban water management problems. Content develops through four case studies. The first looks at analysing water quality to ensure public health. The second considers customer feedback. The third case study introduces smart meter data. The guide flows easily from basic principles through code that, with each case study, increases in complexity. The last case study analyses data using basic machine learning. Readers will be familiar with analysing data but do not need coding experience to use this book. The title will be essential reading for anyone seeking a practical introduction to data science and creating value with R.
Author: Peter Prevos
Publisher: CRC Press
ISBN: 100085647X
Category : Computers
Languages : en
Pages : 212
Get Book Here
Book Description
This addition to the Data Science Series introduces the principles of data science and the R language to the singular needs of water professionals. The book provides unique data and examples relevant to managing water utility and is sourced from the author’s extensive experience. Data Science for Water Utilities: Data as a Source of Value is an applied, practical guide that shows water professionals how to use data science to solve urban water management problems. Content develops through four case studies. The first looks at analysing water quality to ensure public health. The second considers customer feedback. The third case study introduces smart meter data. The guide flows easily from basic principles through code that, with each case study, increases in complexity. The last case study analyses data using basic machine learning. Readers will be familiar with analysing data but do not need coding experience to use this book. The title will be essential reading for anyone seeking a practical introduction to data science and creating value with R.
Author: Pernille Ingildsen
Publisher: IWA Publishing
ISBN: 1780407572
Category : Science
Languages : en
Pages : 307
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Book Description
Today there is increasing pressure on the water infrastructure and although unsustainable water extraction and wastewater handling can continue for a while, at some point water needs to be managed in a way that is sustainable in the long-term. We need to handle water utilities “smarter”. New and effective tools and technologies are becoming available at an affordable cost and these technologies are steadily changing water infrastructure options. The quality and robustness of sensors are increasing rapidly and their reliability makes the automatic handling of critical processes viable. Online and real-time control means safer and more effective operation. The combination of better sensors and new water treatment technologies is a strong enabler for decentralised and diversified water treatment. Plants can be run with a minimum of personnel attendance. In the future, thousands of sensors in the water utility cycle will handle all the complexity in an effective way. Smart Water Utilities: Complexity Made Simple provides a framework for Smart Water Utilities based on an M-A-D (Measurement-Analysis-Decision). This enables the organisation and implementation of “Smart” in a water utility by providing an overview of supporting technologies and methods. The book presents an introduction to methods and tools, providing a perspective of what can and could be achieved. It provides a toolbox for all water challenges and is essential reading for the Water Utility Manager, Engineer and Director and for Consultants, Designers and Researchers.
Author: Dr Peter Prevos
Publisher: Packt Publishing Ltd
ISBN: 1838985506
Category : Computers
Languages : en
Pages : 104
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Book Description
Take the strategic and systematic approach to analyze data to solve business problems Key FeaturesGain detailed information about the theory of data scienceAugment your coding knowledge with practical data science techniques for efficient data analysis Learn practical ways to strategically and systematically use dataBook Description Principles of Strategic Data Science is created to help you join the dots between mathematics, programming, and business analysis. With a unique approach that bridges the gap between mathematics and computer science, this book takes you through the entire data science pipeline. The book begins by explaining what data science is and how organizations can use it to revolutionize the way they use their data. It then discusses the criteria for the soundness of data products and how to best visualize information. As you progress, you’ll discover the strategic aspects of data science by learning the five-phase framework that enables you to enhance the value you extract from data. The final chapter of the book discusses the role of a data science manager in helping an organization take the data-driven approach. By the end of this book, you’ll have a good understanding of data science and how it can enable you to extract value from your data. What you will learnGet familiar with the five most important steps of data scienceUse the Conway diagram to visualize the technical skills of the data science teamUnderstand the limitations of data science from a mathematical and ethical perspectiveGet a quick overview of machine learningGain insight into the purpose of using data science in your workUnderstand the role of data science managers and their expectationsWho this book is for This book is ideal for data scientists and data analysts who are looking for a practical guide to strategically and systematically use data. This book is also useful for those who want to understand in detail what is data science and how can an organization take the data-driven approach. Prior programming knowledge of Python and R is assumed.
Author: Andrea Scozzari
Publisher: Springer Nature
ISBN: 3030619737
Category : Technology & Engineering
Languages : en
Pages : 357
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Book Description
Today, Information and Communication Technologies (ICT) have a pervasive presence in almost every aspect of the management of water. There is no question that the collection of big data from sensing and the insights gained by smart analytics can bring massive benefits. This book focuses on new perspectives for the monitoring, assessment and control of water systems, based on tools and concepts originating from the ICT sector. It presents a portrait of up-to-date sensing techniques for water, and introduces concepts and implications with the analysis of the acquired data. Particular attention is given to the advancements in developing novel devices and data processing approaches. The chapters guide the reader through multiple disciplinary contexts, without aiming to be exhaustive, but with the effort to present relevant topics in such a highly multi-disciplinary framework. This book will be of interest to advanced students, researchers and stakeholders at various levels.
Author: Lucas Bessire
Publisher: Princeton University Press
ISBN: 0691216436
Category : Social Science
Languages : en
Pages : 264
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Book Description
Finalist for the National Book Award An intimate reckoning with aquifer depletion in America's heartland The Ogallala aquifer has nourished life on the American Great Plains for millennia. But less than a century of unsustainable irrigation farming has taxed much of the aquifer beyond repair. The imminent depletion of the Ogallala and other aquifers around the world is a defining planetary crisis of our times. Running Out offers a uniquely personal account of aquifer depletion and the deeper layers through which it gains meaning and force. Anthropologist Lucas Bessire journeyed back to western Kansas, where five generations of his family lived as irrigation farmers and ranchers, to try to make sense of this vital resource and its loss. His search for water across the drying High Plains brings the reader face to face with the stark realities of industrial agriculture, eroding democratic norms, and surreal interpretations of a looming disaster. Yet the destination is far from predictable, as the book seeks to move beyond the words and genres through which destruction is often known. Instead, this journey into the morass of eradication offers a series of unexpected discoveries about what it means to inherit the troubled legacies of the past and how we can take responsibility for a more inclusive, sustainable future. An urgent and unsettling meditation on environmental change, Running Out is a revelatory account of family, complicity, loss, and what it means to find your way back home.
Author: Alla G. Kravets
Publisher: Springer Nature
ISBN: 3031446151
Category : Computers
Languages : en
Pages : 578
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Book Description
This book constitutes the proceedings of the 5th Conference on Creativity in Intellectual Technologies and Data Science, CIT&DS 2023, held in Volgograd, Russia, in September 2023. The 40 regular papers and 2 keynote papers presented were carefully reviewed and selected from 148 submissions. The papers are organized in the following topical sections: Artificial intelligence and deep learning technologies for creative tasks. Knowledge discovery in patent and open sources; Artificial intelligence & Deep Learning Technologies for Creative tasks. Open science semantic technologies; Artificial intelligence and deep learning technologies for creative tasks. Computer vision and knowledge-based control; Cyber-physical systems and big data-driven control: pro-active modeling in intelligent decision making support; Cyber-Physical Systems & Big Data-driven world. Industrial creativity in CASE/CAI/CAD/PDM; Cyber-Physical Systems & Big Data-driven world. Intelligent Internet of Services and Internet of Things; Intelligent Technologies in Social Engineering. Data Science in Social Networks Analysis and Cyber Security; Intelligent Technologies in Social Engineering. Creativity & Game-Based Learning; Intelligent Technologies in Social Engineering. Intelligent Technologies in Medicine& Healthcare; Intelligent Technologies in Social Engineering. Intelligent technologies in Urban Design&Computing.
Author: Yuxiao Dong
Publisher: Springer Nature
ISBN: 3030676676
Category : Computers
Languages : en
Pages : 612
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Book Description
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.
Author: Antony Unwin
Publisher: CRC Press
ISBN: 1040035477
Category : Mathematics
Languages : en
Pages : 447
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Book Description
Data graphics are used extensively to present information. Understanding graphics is a lot about understanding the data represented by the graphics, having a feel not just for the numbers themselves, the reliability and uncertainty associated with them, but also for what they mean. This book presents a practical approach to data visualisation with real applications front and centre. The first part of the book is a series of case studies, each describing a graphical analysis of a real dataset. The second part pulls together ideas from the case studies and provides an overview of the main factors affecting understanding graphics. Key Features: Explains how to get insights from graphics. Emphasises the value of drawing many graphics. Underlines the importance for analysis of background knowledge and context. Readers may be data scientists, statisticians or people who want to become more visually literate. A knowledge of Statistics is not required, just an interest in data graphics and some experience of working with data. It will help if the reader knows something of basic graphic forms such as barcharts, histograms, and scatterplots.
Author: Durgesh Mishra
Publisher: Springer Nature
ISBN: 981999179X
Category :
Languages : en
Pages : 733
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Book Description
Author: M. Niranjanamurthy
Publisher: John Wiley & Sons
ISBN: 111979188X
Category : Computers
Languages : en
Pages : 356
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Book Description
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning and big data. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries. For the purposes of this volume, data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. Although data mining and other related areas have been around for a few decades, data science and analytics are still quickly evolving, and the processes and technologies change, almost on a day-to-day basis. This volume provides an overview of some of the most important advances in these areas today, including practical coverage of the daily applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in these areas, this is a must-have for any library.
