Getting Data Science Done outlines the essential stages in running successful data science projectsproviding comprehensive guidelines to help you identify potential issues and then a range of strategies for mitigating them.
Data science is a field that synthesizes statistics, computer science and business analytics to deliver results that can impact almost any type of process or organization. Data science is also an evolving technical discipline, whose practice is full of pitfalls and potential problems for managers, stakeholders and practitioners. Many organizations struggle to consistently deliver results with data science due to a wide range of issues, including knowledge barriers, problem framing, organizational change and integration with IT and engineering.
Getting Data Science Done outlines the essential stages in running successful data science projects. The book provides comprehensive guidelines to help you identify potential issues and then a range of strategies for mitigating them. The book is organized as a sequential process allowing the reader to work their way through a project from an initial idea all the way to a deployed and integrated product.
| Preface |
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ix | |
| Acknowledgments |
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xi | |
| Introduction |
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xiii | |
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1 | (86) |
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Chapter 1 Getting Started |
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3 | (20) |
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Chapter 2 Project Parameters |
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23 | (4) |
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27 | (16) |
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Chapter 4 Getting Context |
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43 | (8) |
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Chapter 5 Getting Measurements |
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51 | (6) |
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Chapter 6 Consider Interventions |
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57 | (10) |
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Chapter 7 Dwell on Constraints |
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67 | (6) |
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73 | (8) |
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Chapter 9 Getting Success Metrics |
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81 | (6) |
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87 | (108) |
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Chapter 10 Getting Data Updates |
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89 | (6) |
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Chapter 11 Data Familiarity |
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95 | (6) |
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Chapter 12 Data Science Methods |
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101 | (6) |
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Chapter 13 Insights and Analytics |
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107 | (8) |
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Chapter 14 Pattern Discovery |
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115 | (10) |
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Chapter 15 Predictive Modeling |
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125 | (16) |
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141 | (8) |
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Chapter 17 Project Delivery |
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149 | (12) |
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Chapter 18 Estimating ROI |
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161 | (14) |
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175 | (12) |
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Chapter 20 Model Monitoring |
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187 | (8) |
| Conclusion |
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195 | (2) |
| About the Author |
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197 | (2) |
| Index |
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199 | |
John Hawkins is an Australian data scientist with a research background in machine learning for bioinformatics. He holds positions as the Chief Scientist for Ad Tech company Playground XYZ, machine learning advisor for Health Tech start-up HealthVox.org and is an affiliate researcher with the Transitional AI Group. He has 20 years of experience in solving problems in industry and academia, delivering data science solutions for organizations in software development, banking, insurance, media, ad-tech, and bio-medical research. He holds a PhD in Computer Science from the University of Queensland, an Associate Degree in Information Technology from Southern Cross University and a Bachelor of Arts (Honors I) in Philosophy from the University of Newcastle. He has written more than 20 peer-reviewed academic articles and presented at academic and industry conferences around the world.
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