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.

This addition to the Data Science Series introduces the principles of data science and R 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 three case studies. The first looks at analysing water quality to ensure public health. The second considers customer feedback. The final case study introduces smart meter data. The guide flows easily from basic principles through code that, with each case study, increases in complexity. 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.

Take the strategic and systematic approach to analyze data to solve business problems Key Features Gain detailed information about the theory of data science Augment your coding knowledge with practical data science techniques for efficient data analysis Learn practical ways to strategically and systematically use data Book DescriptionPrinciples of Strategic Data Science is created to help you join the dots between mathematics, programming, and business analysis. With a unique approach that bridges the gap between mathematics and computer science, this book takes you through the entire data science pipeline. The book begins by explaining what data science is and how organizations can use it to revolutionize the way they use their data. It then discusses the criteria for the soundness of data products and how to best visualize information. As you progress, you'll discover the strategic aspects of data science by learning the five-phase framework that enables you to enhance the value you extract from data. The final chapter of the book discusses the role of a data science manager in helping an organization take the data-driven approach. By the end of this book, you'll have a good understanding of data science and how it can enable you to extract value from your data. What you will learn Get familiar with the five most important steps of data science Use the Conway diagram to visualize the technical skills of the data science team Understand the limitations of data science from a mathematical and ethical perspective Get a quick overview of machine learning Gain insight into the purpose of using data science in your work Understand the role of data science managers and their expectations Who this book is forThis book is ideal for data scientists and data analysts who are looking for a practical guide to strategically and systematically use data. This book is also useful for those who want to understand in detail what is data science and how can an organization take the data-driven approach. Prior programming knowledge of Python and R is assumed.