Название: Data Science for Water Utilities: Data as a Source of Value
Автор: Peter Prevos
Издательство: CRC Press
Год: 2023
Страниц: 212
Язык: английский
Формат: pdf (true)
Размер: 31.5 MB

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.

Who Is This Book For?
This book’s content started as a course syllabus to teach water professionals how to use the R language. While the case studies are specific to the types of problems faced by water utilities, the principles of solving problems with code apply to anyone wanting to improve their skills in data analysis. This book is thus helpful for anyone interested to learn how to use the R language to systematically analyse data.

This book does not only show how to write code but also how to apply best-practice principles of data analysis and visualisation. Learning how to code is pretty straightforward, but using it to create water management outcomes requires additional skills. This book, therefore, provides a framework to produce sound, useful, and aesthetic data products. This chapter discusses the principles of this framework, which are applied with R code in the remainder of the book.

R is a popular programming language specially designed for data analysis with built-in statistical capabilities. The internet is awash with discussions on whether R or Python is better for Data Science. These discussions are like arguing whether Dutch or English are better for writing books. All languages, either natural or artificial, have their strengths and weaknesses. This book is based on principles, and the skills taught in these chapters are easily transferrable to Python or other programming languages.

Prerequisites:
To benefit from this book, you must have some prior knowledge and experience with analysing data and statistics, either spreadsheets or otherwise. Experience with writing computer code is helpful but not necessary, as the book starts from the basic principles. Likewise, knowledge of water management is not required as the context of the case studies is explained in sufficient detail. Some knowledge of statistics is also helpful. Lastly, you will need access to the R language for statistical computing and the RStudio interface.