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: 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: Andrea Scozzari
Publisher: Springer Nature
ISBN: 3030619737
Category : Technology & Engineering
Languages : en
Pages : 342
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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: 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: Rakesh K. Jain
Publisher: CRC Press
ISBN: 1000873463
Category : Computers
Languages : en
Pages : 251
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Book Description
This book explains use of data science-based techniques for modeling and providing optimal solutions to complex problems in civil engineering. It discusses civil engineering problems like air, water and land pollution, climate crisis, transportation infrastructures, traffic and travel modes, mobility services, and so forth. Divided into two sections, the first one deals with the basics of data science and essential mathematics while the second section covers pertinent applications in structural and environmental engineering, construction management, and transportation. Features: Details information on essential mathematics required to implement civil engineering applications using data science techniques. Discusses broad background of data science and its fundamentals. Focusses on structural engineering, transportation systems, water resource management, geomatics, and environmental engineering. Includes python programming libraries to solve complex problems. Addresses various real-world applications of data science based civil engineering use cases. This book aims at senior undergraduate students in Civil Engineering and Applied Data Science.
Author: Regina Bispo
Publisher: Springer Nature
ISBN: 3031127668
Category : Mathematics
Languages : en
Pages : 364
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Book Description
This volume presents a collection of twenty-five peer-reviewed articles carefully selected from the contributions presented at the XXV Congress of the Portuguese Statistical Society (2021). Containing state-of-the-art developments in theoretical and applied statistics, the book will be accessible to readers with a background in mathematics and statistics, but will also be of interest to researchers from other scientific disciplines (e.g., biology, economics, medicine), who will find a broad range of relevant applications.
Author: Bradley J. Congelio
Publisher: CRC Press
ISBN: 1003813267
Category : Sports & Recreation
Languages : en
Pages : 383
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Book Description
It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans, and commentators, throughout broadcasts, regularly use terms such as air yards, CPOE, and EPA on a casual basis. This rapid growth, combined with an increasing accessibility to NFL data, has helped create a burgeoning amateur analytics movement, highlighted by the NFL’s annual Big Data Bowl. Because learning a coding language can be a difficult enough endeavor, Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to, opting to provide step-by-step instructions in a structured, jargon-free manner. Key Coverage: Installing R, RStudio, and necessary packages Working and becoming fluent in the tidyverse Finding meaning in NFL data with examples from all the functions in the nflverse family of packages Using NFL data to create eye-catching data visualizations Building statistical models starting with simple regressions and progressing to advanced machine learning models using tidymodels and eXtreme Gradient Boosting The book is written for novices of R programming all the way to more experienced coders, as well as audiences with differing expected outcomes. Professors can use Introduction to NFL Analytics with R to provide data science lessons through the lens of the NFL, while students can use it as an educational tool to create robust visualizations and machine learning models for assignments. Journalists, bloggers, and arm-chair quarterbacks alike will find the book helpful to underpin their arguments by providing hard data and visualizations to back up their claims.
Author: Martin Hugh Monkman
Publisher: CRC Press
ISBN: 1040019137
Category : Business & Economics
Languages : en
Pages : 205
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Book Description
The Data Preparation Journey: Finding Your Way With R introduces the principles of data preparation within in a systematic approach that follows a typical data science or statistical workflow. With that context, readers will work through practical solutions to resolving problems in data using the statistical and data science programming language R. These solutions include examples of complex real-world data, adding greater context and exposing the reader to greater technical challenges. This book focuses on the Import to Tidy to Transform steps. It demonstrates how “Visualise” is an important part of Exploratory Data Analysis, a strategy for identifying potential problems with the data prior to cleaning. This book is designed for readers with a working knowledge of data manipulation functions in R or other programming languages. It is suitable for academics for whom analyzing data is crucial, businesses who make decisions based on the insights gleaned from collecting data from customer interactions, and public servants who use data to inform policy and program decisions. The principles and practices described within The Data Preparation Journey apply regardless of the context. Key Features: Includes R package containing the code and data sets used in the book Comprehensive examples of data preparation from a variety of disciplines Defines the key principles of data preparation, from access to publication
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: 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.
