Data Science for Water Utilities: Data as a Source of Value

by Peter Prevos

Overview

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.

Product Details

ISBN-13:	9781032354552
Publisher:	CRC Press
Publication date:	05/10/2023
Series:	Chapman & Hall/CRC Data Science Series
Pages:	211
Product dimensions:	7.00(w) x 10.00(h) x (d)

About the Author

Peter Prevos is a civil engineer and social scientist who manages the data science function at Coliban Water in regional Australia. Peter has three decades of experience in water management in Europe, Asia and Australia. He promotes creating value from data using code. He is the author of several books and runs leading courses in data science for water professionals.

Table of Contents

1. Introduction to Data Science 2. Basics of the R Language 3. Loading and Exploring Data 4. Descriptive Statistics 5. Visualising Data with ggplot2 6. Sharing Results 7. Managing Dirty Data 8. Analysing the Customer Experience 9. Basic Linear Regression 10. Clustering Customers to Define Segments 11. Working with Dates and Times 12. Detecting Outliers and Anomalies 13. Introduction to Machine Learning