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Winning with Data Science: A Handbook for Business Leaders [Kõva köide]

4.47/5 (34 hinnangut Goodreads-ist)
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  • Formaat: Hardback, 272 pages, kõrgus x laius: 216x140 mm, 13 figures
  • Ilmumisaeg: 30-Jan-2024
  • Kirjastus: Columbia University Press
  • ISBN-10: 0231206860
  • ISBN-13: 9780231206860
Teised raamatud teemal:
Winning with Data Science: A Handbook for Business LeadersSuurem pilt
  • Formaat: Hardback, 272 pages, kõrgus x laius: 216x140 mm, 13 figures
  • Ilmumisaeg: 30-Jan-2024
  • Kirjastus: Columbia University Press
  • ISBN-10: 0231206860
  • ISBN-13: 9780231206860
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780231206860.html
Märksõnad:
"Data science is increasingly important in the business world, not just for the teams in charge of implementing it but the professionals adjacent to them. Yet not all businesspeople have a general understanding of the basics-and if senior management assigns them to work alongside a data science team, they'll need that knowledge as soon as possible without having to take online courses or dive down the Internet rabbit hole. This book provides that knowledge base, walking readers through the key ideas needed to communicate and work with a data science team. They will be able to understand the basic technical lingo, recognize the types of talent on the team and pose good questions to your data scientists to open up more insights, create opportunities, and generate value. By the end of the book they will be able to answer key questions including how data is collected and stored, what hardware and software tools are needed to analyze data, who does what on the data science team and which models should be considered for specific projects. Most critically, they will also be armed with critical questions that you can use to further probe data analysts, statisticians, data scientists and other technical experts to better understand the value of their work for a business"--

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.

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.

Arvustused

Engaging in data science requires diplomacy for maximal impact. Namely, understanding the norms and priorities of data professionals helps you to spot risks and opportunities. As experienced, trusted data science advisors, and by providing valuable examples, Friedman and Swaminathan open a new data-driven world that spans every single industry vertical. -- Armen Kherlopian, CEO and Partner, Covenant Venture Capital Winning with Data Science is refreshingly practical and clear. Its also fun and empowering. After reading it, youll be more savvy about working with data teams and more valuable to your company. You may even become the envy of your colleagues (and competitors), who will wonder how you got so smart. -- Steven Strogatz, Susan and Barton Winokur Distinguished Professor for the Public Understanding of Science and Mathematics, Cornell University, and author of Infinite Powers Friedman and Swaminathan have taken the complex topic of data science and made it accessible to everyone. Their creative use of characters, situations, and meaningful examples serve to demystify how to think about the field, how to use data science to solve everyday problems, and how to interact with data scientists to ensure successful projects. An excellent read, even for people who (think they) know a little about the field of data science! -- Melvin (Skip) Olson, global head, Integrated Evidence Strategy and Innovation, Novartis Pharma AG Winning with Data Science addresses a critical but often ignored obstacle in data science: the knowledge gap between business stakeholders and technical teams. This book cuts through data science buzzwords and empowers readers with the knowledge to cultivate thriving data cultures. Distinguishing itself from others, this book prioritizes effective communication and collaboration within the data science sphere, facilitating deeper discussions on intricate technical subjects. -- Jeff Chen, former chief data scientist of the U.S. Department of Commerce and coauthor of Data Science for Public Policy A terrific work. Winning with Data Science expertly takes readers through daily 'data lives,' struggles with business problems, and the data science concepts that can help address them. -- Paul W. Thurman, Columbia University Mailman School of Public Health, and author of MBA Fundamentals: Statistics Friedman and Swaminathan provide a deep understanding of data science methodologies to managers, striking exactly the right balance of complexity and accessibility. -- Kim Sweeny, Principal Projects Officer, Institute for Sustainable Industries & Liveable Cities, Victoria University In today's digital age, data is king. And for business leaders, extracting insights and using them to drive informed decisions is more crucial than ever. . . . If [ you] want to speak the language of data and harness its potential, Winning with Data Science is a must-read. -- Ken Kuang, entrepreneur, and Founder, Torrey Hills Technologies By the end of the book, you'll feel like a pro in talking about data, even if you're not a tech expert. -- Nirali Mehta, Founder and CEO, PHARMA-STATS Winning with Data Science tackles the complex topic of data science and simplifies it to make it accessible to anyone, enabling a more data-driven culture at your organization. -- David Mathison, CEO, Chief AI Officer Summit, CDO Club, and CDO Summit Impressively informative, exceptionally well written, and thoroughly 'reader friendly' in both organization and presentation, Winning with Data Science: A Handbook for Business Leaders is essential reading for anyone who works with data science teams or is interested in the practical side of the subject. * Midwest Book Review * A top 15 'best data science book.' * 365 Data Science *

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
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.