Getting data science done : managing projects from ideas to products / John Hawkins.

Format:

Book

Author/Creator:

Hawkins, John, 1719-1789, author.

Language:

English

Subjects (All):

Big data.

Data mining.

Project management.

Physical Description:

1 online resource (220 pages)

Edition:

1st ed.

Place of Publication:

New York, NY : Business Expert Press, LLC, [2023]

Summary:

Getting Data Science Done outlines the essential stages in running successful data science projects-providing 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.

Contents:

Cover

Halftitle

Title

Copyright

Description

Contents

Preface

Acknowledgments

Introduction

Part I: Problem Framing

Chapter 1: Getting Started

Chapter 2: Project Parameters

Chapter 3: Getting Buy-In

Chapter 4: Getting Context

Chapter 5: Getting Measurements

Chapter 6: Consider Interventions

Chapter 7: Dwell on Constraints

Chapter 8: Project Focus

Chapter 9: Getting Success Metrics

Part II: Execution

Chapter 10: Getting Data Updates

Chapter 11: Data Familiarity

Chapter 12: Data Science Methods

Chapter 13: Insights and Analytics

Chapter 14: Pattern Discovery

Chapter 15: Predictive Modeling

Chapter 16: Model Context

Chapter 17: Project Delivery

Chapter 18: Estimating ROI

Chapter 19: Deployment

Chapter 20: Model Monitoring

Conclusion

About the Author

Index

Adpage

Backcover.

Notes:

Includes index.

Description based on print version record.

Other Format:

Print version: Hawkins, John Getting Data Science Done

ISBN:

9781637422786