Data Science for Water Utilities Data as a Source of Value

Data Science for Water Utilities: Data as a Source of Value by Peter Prevos, published by CRC Press in 2023, is a practical guide tailored for water professionals. This edition spans 197 pages and introduces the principles of data science alongside the R programming language, specifically addressing the unique challenges faced in managing water utilities. The book draws on the author’s extensive experience, providing relevant data and examples to support its teachings.
Readers will find a structured approach that develops through four case studies, covering topics such as water quality analysis, customer feedback, and smart meter data. The content progresses from basic principles to more complex coding, culminating in an exploration of basic machine learning techniques. While familiarity with data analysis is beneficial, prior coding experience is not required, making this book accessible for those looking to apply data science in urban water management.
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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.
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