Winning With Data Science - A Handbook for Business Leaders

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.

Table des matières

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

A propos de l'auteur

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.

Résumé

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.

Détails du produit

• Auteurs: Howard Steven Friedman, Howard Steven Swaminathan Friedman, Akshay Swaminathan
• Edition: Columbia University Press
• Langues: Anglais
• Format d'édition: Livre Relié
• Sortie: 12.12.2023
• EAN: 9780231206860
• ISBN: 978-0-231-20686-0
• Pages: 272
• Catégories: Sciences naturelles, médecine, informatique, technique > Informatique, ordinateurs > Applications, programmes; Sciences sociales, droit, économie > Economie > Gestion; Product Design, BUSINESS & ECONOMICS / Project Management, DESIGN / General, BUSINESS & ECONOMICS / Management, Management: leadership & motivation, Project management, Management: leadership and motivation, Management decision making, Information technology: general issues, Business applications, COMPUTERS / Data Science / Data Analytics, Computer applications in industry and technology, Data capture and analysis, Data Capture & Analysis, Computer aided manufacture (CAM)