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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.
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
| Erscheinungsjahr: | 2024 |
|---|---|
| Fachbereich: | Management |
| Genre: | Importe, Wirtschaft |
| Rubrik: | Recht & Wirtschaft |
| Medium: | Buch |
| ISBN-13: | 9780231206860 |
| ISBN-10: | 0231206860 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Friedman, Howard Steven
Swaminathan, Akshay |
| Hersteller: |
Columbia Univers. Press
Columbia Business School Publishing |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Abbildungen: | 13 figures |
| Maße: | 220 x 147 x 25 mm |
| Von/Mit: | Howard Steven Friedman (u. a.) |
| Erscheinungsdatum: | 30.01.2024 |
| Gewicht: | 0,504 kg |
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