Principles of Strategic Data Science
Principles of Strategic Data Science
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Principles of Strategic Data Science

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Completed on 2019-03-08

About the Book

Principles of Strategic Data Science describes a framework to create value from data to help organisations meeting their objectives. This book is written for managers interested in strategic data science and data scientists interested in how to improve their contribution to their organisation.

The first chapter defines data science as a strategic and systematic approach to business analysis. Data science happens in the confluence of domain knowledge and mathematics, enabled by computer science.

Chapter two presents a model for good data science as being useful, sound and aesthetic. Useful data science adds value to an organisation and soundness relates to a systematic approach to analysis. Data science needs to be aesthetic to ensure that users understand the results.

The penultimate chapter discusses a strategic framework for maximising the value of data assets through an evolutionary approach from data management to prescriptive analysis. This evolution starts with good data management and continues through to using algorithms to make business decisions.

The final chapter of this book discusses various aspects of developing a data-driven organisation. Data science is not about lone geniuses but is undertaken by teams. Principles of Strategic Data Science closes with reflections on the limitations and ethics of data science.

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About the Author

Peter Prevos
Peter Prevos

As a young boy, Peter Prevos always wanted to be a magician. He even shared the stage with Hans Klok, a famous Dutch magician. Hans won the competition, and Peter came last.

Peter abandoned his dream to be a professional magician and became a carpenter. He never achieved this goal because his teachers said that he was too clumsy to swing a hammer. Heeding their advice, Peter attended university and studied architecture. But alas, after one year behind the drawing board, the lecturer suggested that he wasn’t artistic enough to be an architect.

Not perturbed by this third disappointment, Peter eventually became a civil engineer and built structures, islands, harbours, bridges, and pipelines around the world.

Early in his technical career, however, he decided not to be technically minded enough to be an engineer. Peter moved on to study philosophy and a PhD in management. In his professional life, he now manages the data science team for a water utility in regional Australia.

Peter’s interest in magic tricks and philosophy never waned, and in his spare time, he writes books and articles about management, philosophy and magic.

Reader Testimonials

Michael Thomas
Michael Thomas

If you want to know about data science, Peter Prevos is your man. Not ‘just’ an engineer, Peter is equally conversant across the humanities and social sciences. His unique perspective is delivered with passion in his latest book; weaving a wonderful account of both the ‘science’ and the ‘art’ behind big data. A must read for those seeking a far richer and contextualised account of data-science.

Table of Contents

  • Preface
    • Preface to second edition
  • Acknowledgements
  • 1. What is Data Science?
    • 1.1 Data-Driven Organisation
    • 1.2 The Data Revolution
    • 1.3 The Elements of Data Science
    • 1.4 The Purpose of Data Science
  • 2. Good Data Science
    • 2.1 Data Science Trivium
    • 2.2 Useful Data Science
    • 2.3 Sound Data Science
    • 2.4 Aesthetic Data Science
    • 2.5 Best-Practice Data Science
  • 3. Strategic Data Science
    • 3.1 The Data Science Continuum
    • 3.2 Collect data
    • 3.3 Descriptive Statistics
    • 3.4 Diagnostics
    • 3.5 Predicting the Future
    • 3.6 Prescribe Action
    • 3.7 Towards a Data-Driven Organisation
  • 4. The Data-Driven Organisation
    • 4.1 People
    • 4.2 Systems
    • 4.3 Process
    • 4.4 The Limitations of Data Science
  • 5. References
  • Notes

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