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.
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.
This is a great book for high level and conceptual discussion on data science. Good for decision-makers that rely on data for deciding company growth. How they can use the data and being vigilant with it.
- Preface to second edition
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
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