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.