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Managing Data Science

You're reading from  Managing Data Science

Product type Book
Published in Nov 2019
Publisher Packt
ISBN-13 9781838826321
Pages 290 pages
Edition 1st Edition
Languages
Author (1):
Kirill Dubovikov Kirill Dubovikov
Profile icon Kirill Dubovikov
Toc

Table of Contents (18) Chapters close

1. Section 1: What is Data Science?
2. What You Can Do with Data Science 3. Testing Your Models 4. Understanding AI 5. Section 2: Building and Sustaining a Team
6. An Ideal Data Science Team 7. Conducting Data Science Interviews 8. Building Your Data Science Team 9. Section 3: Managing Various Data Science Projects
10. Managing Innovation 11. Managing Data Science Projects 12. Common Pitfalls of Data Science Projects 13. Creating Products and Improving Reusability 14. Section 4: Creating a Development Infrastructure
15. Implementing ModelOps 16. Building Your Technology Stack 17. Conclusion 18. Other Books You May Enjoy

Why do big organizations fail so often?

Big companies often see innovations as a gold mine. Many entrepreneurs think, Have you heard about this new artificial intelligence start-up? Just imagine what we can achieve in this field. Our resources are vast compared to other companies. However, history dictates otherwise, since emerging innovative technologies are often born inside small start-ups rather than in big, stable businesses. This is counterintuitive. Large companies have more resources, people, time, and risk immunity, while start-ups have almost none. Then why do big corporations fail at innovation? The Innovator's Dilemma, by Clayton Christensen (https://www.amazon.com/Innovators-Dilemma-Revolutionary-Change-Business/dp/0062060244), and Crossing the Chasm, by Geoffrey Moore (https://www.amazon.com/Crossing-Chasm-3rd-Disruptive-Mainstream/dp/0062292986), tell a convincing...

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