Winning with Data Science
A Handbook for Business Leaders
1st Edition
Published on 5. February 2024
272 pages
978-0-231-55669-9 (ISBN)
Description
Whether you are a newly minted MBA or a project manager at a Fortune 500 company, data science will play a major role in your career. Knowing how to communicate effectively with data scientists in order to obtain maximum value from their expertise is essential. This book is a compelling and comprehensive guide to data science, emphasizing its real-world business applications and focusing on how to collaborate productively with data science teams.
Taking an engaging narrative approach, Winning with Data Science covers the fundamental concepts without getting bogged down in complex equations or programming languages. It provides clear explanations of key terms, tools, and techniques, illustrated through practical examples. The book follows the stories of Kamala and Steve, two professionals who need to collaborate with data science teams to achieve their business goals. Howard Steven Friedman and Akshay Swaminathan walk readers through each step of managing a data science project, from understanding the different roles on a data science team to identifying the right software. They equip readers with critical questions to ask data analysts, statisticians, data scientists, and other technical experts to avoid wasting time and money. Winning with Data Science is a must-read for anyone who works with data science teams or is interested in the practical side of the subject.
Taking an engaging narrative approach, Winning with Data Science covers the fundamental concepts without getting bogged down in complex equations or programming languages. It provides clear explanations of key terms, tools, and techniques, illustrated through practical examples. The book follows the stories of Kamala and Steve, two professionals who need to collaborate with data science teams to achieve their business goals. Howard Steven Friedman and Akshay Swaminathan walk readers through each step of managing a data science project, from understanding the different roles on a data science team to identifying the right software. They equip readers with critical questions to ask data analysts, statisticians, data scientists, and other technical experts to avoid wasting time and money. Winning with Data Science is a must-read for anyone who works with data science teams or is interested in the practical side of the subject.
More details
Other editions
Additional editions
Howard Steven Friedman | Akshay Swaminathan
Winning with Data Science
A Handbook for Business Leaders
Book
01/2024
Columbia University Press
€26.50
Shipment within 10-20 days
Persons
Howard Steven Friedman, an adjunct professor at Columbia University, is a data scientist with decades of experience leading analytics projects in the private and public sectors. His previous books, including Ultimate Price (2020) and Measure of a Nation (2012), have been translated into many languages and featured on national media.
Akshay Swaminathan is a data scientist who works on strengthening health systems. He has more than forty peer-reviewed publications, and his work has been featured in the New York Times and STAT. Previously at Flatiron Health, he currently leads the data science team at Cerebral and is a Knight-Hennessy scholar at Stanford University School of Medicine.
Akshay Swaminathan is a data scientist who works on strengthening health systems. He has more than forty peer-reviewed publications, and his work has been featured in the New York Times and STAT. Previously at Flatiron Health, he currently leads the data science team at Cerebral and is a Knight-Hennessy scholar at Stanford University School of Medicine.
Content
Acknowledgments
Introduction
1. Tools of the Trade
2. The Data Science Project
3. Data Science Foundations
4. Making Decisions with Data
5. Clustering, Segmenting, and Cutting Through the Noise
6. Building Your First Model
7. Tools for Machine Learning
8. Pulling It Together
9. Ethics
Conclusion
Notes
Index
Introduction
1. Tools of the Trade
2. The Data Science Project
3. Data Science Foundations
4. Making Decisions with Data
5. Clustering, Segmenting, and Cutting Through the Noise
6. Building Your First Model
7. Tools for Machine Learning
8. Pulling It Together
9. Ethics
Conclusion
Notes
Index
