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Managing reliable water services requires not only a sufficient volume of water, but also large amounts of data. Water professionals measure the flow and quality of the water and how customers perceive their service. This course teaches the basics of data science using the R language and the Tidyverse libraries to analyse water management problems.
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The term 'digital water utility' has become a popular buzzword in the industry. A digital utility can only exist when the people that manage the water improve their skills. This workshop introduces participants to the principles of data science and analysing data using the R language for statistical computing.
Participants will learn about the principles of data science and the basics of using the R language. This course focuses on providing a broad introduction to creating value from data. The objective of this course is to inspire water professionals to apply data science principles and undertake further research to create value from data.
Principles of Water Utility Data Science
The first session introduces a framework for best practice in analysing data and sharing the results. This framework derives from the book Principles of Strategic Data Science. The three case studies each implement aspects of this framework.
Introduction to the R Language
The second session introduces the basic principles of the R language and the Tidyverse method and applies these principles to compute the flow in an open water supply channel.
Case Study 1: Water Quality Regulations
In this first case study, participants apply their skills to laboratory testing data from an imaginary drinking water network. The case study revolves around checking the data for compliance with water quality regulations. Participants will analyse and visualise the data and create an automated PowerPoint presentation based on the data.
Case Study 2: Understanding Customer Perception
The data for the second case study consists of the results of a survey of American consumers about their perception of tap water services. Participants use the Tidyverse to clean, transform and visualise this data.
Case Study 3: Analysing Water Consumption
In the last case study, participants use the analytical functionalities of the Tidyverse to analyse data from smart meters to find anomalies in water consumption.