Data Science for Water Professionals (The Course)


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Course Info

This course includes 1 attempt.

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 in their analysis and use computer code to solve problems.

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 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 applies these principles to compute the flow in an open water supply channel. The remainder of the course follows two case studies.

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 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. The case study ends with participants creating a MS Word report to communicate the results of analysis.



  • R

Course Material

  • 1. Data Science for Water Professionals
  • Learning Objectives
  • Prerequisites
  • Case Study approach
  • Participant Activities
  • 2. Principles of Water Utility Data Science
  • What is data science?
  • The Elements of Data Science
  • The Water-Data Value Chain: The Digital Water Utility
  • Data Science Tools
  • Good Data Science
  • Best-Practice Data Science with R
  • Further Study
  • 3. Introduction to the R Language
  • The R Language
  • Basic principles of a programming
  • Debugging Code
  • Using R and RStudio
  • Basics of the R language
  • RStudio scripts and projects
  • Quiz 1: Calculating channel flows
  • icon/quiz Created with Sketch.
    Quiz 1: Channel Flow Measurements10 attempts allowed
  • Further Study
  • 4. Exploring Data with the Tidyverse Packages
  • R Libraries
  • Introducing the Tidyverse
  • Case Study 1
  • Exploring the Case Study Data
  • Quiz 2: Exploring Water Quality Data
  • icon/quiz Created with Sketch.
    Quiz 2: Exploring data10 attempts allowed
  • Further Study
  • 5. Descriptive Statistics
  • Problem Statement
  • Analyse the Data
  • Calculating Percentiles
  • Analysing Grouped Data
  • Quiz 3: Analysing Water Quality Data
  • icon/quiz Created with Sketch.
    Quiz 3: Analysing Water Quality Data10 attempts allowed
  • Further Study
  • 6. Visualising Data with ggplot
  • Principles of Visualisation
  • Visualising data with ggplot
  • Sharing visualisations
  • Assignment
  • Further Study
  • 7. Creating Data Products
  • Data Science Workflow
  • Reproducible Research
  • R-Markdown
  • Mini Hackathon
  • 8. Cleaning Data
  • Case Study 2
  • Cleaning data
  • Joining data frames
  • Simplifying Code with Data Pipes
  • Quiz 4: Cleaning Data
  • icon/quiz Created with Sketch.
    Quiz 4: Cleaning data10 attempts allowed
  • Further Study
  • 9. Exploring the Customer Experience
  • Consumer Involvement
  • Missing Data
  • Preparing the Involvement Data
  • Tidy Data
  • Quiz 5: Transforming data
  • icon/quiz Created with Sketch.
    Quiz 5: Transforming data10 attempts allowed
  • Further Study
  • 10. Analysing the customer experience
  • The Reliability and Validity of Surveys
  • Correlations
  • Hierarchical Clustering to assess validity
  • Reviewing the Personal Involvement Index
  • Quiz
  • icon/quiz Created with Sketch.
    Quiz 6: Correlations and Clustering10 attempts allowed
  • Further study
  • 11. Writing Reports
  • Creating documents with RMarkdown
  • Survey Items
  • Mini Hackathon
  • In Closing
  • Searching for answers
  • Forums
  • Further Study
  • Thanks


    • Dr Peter Prevos is a civil engineer and social scientist who also dabbles in theatrical magic. Peter has almost three decades of experience as a water engineer and manager, working in Europe, Africa, Asia and Australia. He has worked on marine engineering, drinking water an sewage treatment projects. Throughout his career analysing data has been a central theme.

      He also a PhD in business and is the author of Customer Experience Management for Water Utilities (IWA Publishing) and Principles of Strategic Data Science (Packt Publishing). In his work, Peter combines the social sciences with engineering to create value for customers.

      He is currently managing the data science function for a water utility in regional Australia. The objective of this strategy is to create value from data through useful, sound and aesthetic data science. His mission is to breed unicorn data scientists by motivating other water professionals to ditch their spreadsheets and learn how to write code.


This course has a private forum for learners who are taking this course.

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