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Principles of Strategic Data Science

Principles of Strategic Data Science

By : Peter Prevos
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Principles of Strategic Data Science

Principles of Strategic Data Science

5 (1)
By: Peter Prevos

Overview of this book

Mathematics and computer science form an integral part of data science, and understanding them is crucial for efficiently managing data. This book is designed to take you through the entire data science pipeline and help you join the dots between mathematics, programming, and business analysis. You’ll start by learning what data science is and how organizations can use it to revolutionize the way they use their data. The book then covers the criteria for the soundness of data products and demonstrates how to effectively visualize information. As you progress, you’ll discover the strategic aspects of data science by exploring the five-phase framework that enables you to enhance the value you extract from data. Toward the concluding chapters, you’ll understand 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.
Table of Contents (6 chapters)
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Acknowledgements

My career has been dynamic and varied but analyzing data has been the one constant in my career. My toolkit to convert data to actionable intelligence has evolved significantly over the past twenty-five years. My former and current colleagues helped me to accumulate new approaches and skills, which led me to write this book.

In my first job as an engineer, Wim van Vliet in Johannesburg was a significant influence on my development as an engineer. Through him, I learned how to convert my theoretical knowledge into a systematic approach to solving real problems.

René Zekveld was my manager at Boskalis when I worked on marine engineering projects in Hong Kong and Bangladesh. René taught me tricks of the trade on how to interpret data to achieve sound business outcomes. He emphasized the importance of the relationship between reality and data. Our long discussions about data showed me that actionable intelligence and insight are essential outcomes.

After my international career, I moved back to the Netherlands and spent time with Rijkswaterstaat, the government agency responsible for keeping the country from flooding. Working for this organization exposed me to probabilistic approaches in cost estimation and project management. We regularly worked with mathematicians, whose names are lost in time, to help us grasp the complexity of our analysis. Working for this organization advanced my skills in using mathematics to create value from data.

At the start of the new millennium, I started my current job at Coliban Water, a water utility in regional Australia. Brad Dole, a former IT manager, has been influential in how I now work with data. As an IT manager, Brad provided me with the freedom to maximize the capabilities of the company hardware. Brad also has a razor-sharp insight into data and how to convert business problems into code. Under Brad's guidance, we developed several in-house software solutions, which took my software development skills beyond a mere hobby.

Jenny Fogarty is my current colleague and the data architect at Coliban Water. We have worked on developing software and reporting mechanisms for many years. Through her expertise, I learned everything I know about managing data. It was on her advice five years ago that I started researching the topic of data science. Not only has her advice helped me to complete my dissertation about customer centricity, but it has also transformed the way I view my profession.

No vision of data science can ever be implemented without people doing the work. Gary Schurr has over the past few years been great at finding innovative ways to report information. His critical helps to straighten out some of my more impulsive ideas.

I started my formal journey into data science by doing a range of courses on the Coursera website. The data science specialization by John Hopkins University taught me the principles of how to use the R language for statistical computing. The inspiring lectures by Roger Peng, Jeff Leek and Brian Caffo helped me to think more systematically in how I deal with data.

My manager David Sheehan often encourages me to develop new ideas and 'conquer the world'. This book is just another small step in that direction. It would not have existed without the freedom David provides me to shape the data science function at Coliban Water and the broader water industry in Australia.

Lastly, I thank all my colleagues at Coliban Water and the broader Australian water industry who indulge me in my geekiness and enthusiasm to find data solutions to improve the way we service our customers. This book is the direct result of the positive feedback from the people that have attended my conference presentations and read my publications and articles on my Lucid Manager—https://lucidmanager.org website.

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