Data Science and Analytics Strategy

This book describes how to establish data science and analytics capabilities in organisations using emergent design, an evolutionary approach that increases the chances of successful outcomes while minimising upfront investment.

Autorentext

Kailash Awati is a data and sensemaking professional with a deep interest in helping organisations tackle complex problems. He is an Adjunct Fellow in Human-Centred Data Science at the UTS Connected Intelligence Centre and a Data and Insights Manager at a government agency. Over the last decade, he has established data capabilities in diverse organisations using the principles described in this book. In addition to his work in industry, he has developed and taught postgraduate courses in machine learning and decision-making under uncertainty. He is the co-author of two well-regarded books on managing socially complex problems in organisations: The Heretic's Guide to Best Practices and The Heretic's Guide to Management.

Alexander Scriven is a senior data scientist at Atlassian in Sydney, Australia, and has experience across start-ups, government, and enterprise building analytical capacities and executing on data science projects. He greatly enjoys teaching and mentoring and has built and delivered both master's-level university courses in machine learning and deep learning and highly rated courses for online platforms such as Datacamp. His research interests are in applying data science techniques to novel industry challenges. Alex greatly enjoys bridging the gap between cutting-edge technology and business applications.

Inhalt

Foreword. Preface. Acknowledgements. Contributors. 1. Introduction. 2. What Is Data Science? 3. The Principles of Emergent Design. 4. Charting a Course. 5. Capability and Culture. 6. Technical Choices. 7. Doing Data Science: From Planning to Production. 8. Doing the Right Thing. 9. Coda. Index.