Data Science for Water Utilities
eBook - ePub

Data Science for Water Utilities

Data as a Source of Value

  1. 197 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Data Science for Water Utilities

Data as a Source of Value

About this book

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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Yes, you can access Data Science for Water Utilities by Peter Prevos in PDF and/or ePUB format, as well as other popular books in Betriebswirtschaft & Programmierung von Spielen. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Contents
  7. Preface
  8. Foreword
  9. 1 Introduction
  10. 2 Basics of the R Language
  11. 3 Loading and Exploring Data
  12. 4 Descriptive Statistics
  13. 5 Visualising Data with ggplot2
  14. 6 Sharing Results
  15. 7 Managing Dirty Data
  16. 8 Analysing the Customer Experience
  17. 9 Basic Linear Regression
  18. 10 Clustering Customers to Define Segments
  19. 11 Working with Dates and Times
  20. 12 Detecting Outliers and Anomalies
  21. 13 Introduction to Machine Learning
  22. 14 In Closing
  23. Bibliography
  24. Index