Getting Data Science Done: Managing Projects from Ideas to Products

  • 3h 33m
  • John Hawkins
  • Business Expert Press
  • 2022

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.

About the Author

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 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.

In this Book

  • Preface
  • Introduction
  • Getting Started
  • Project Parameters
  • Getting Buy-In
  • Getting Context
  • Getting Measurements
  • Consider Interventions
  • Dwell on Constraints
  • Project Focus
  • Getting Success Metrics
  • Getting Data Updates
  • Data Familiarity
  • Data Science Methods
  • Insights and Analytics
  • Pattern Discovery
  • Predictive Modeling
  • Model Context
  • Project Delivery
  • Estimating ROI
  • Deployment
  • Model Monitoring
  • Conclusion