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Managing Data Science

You're reading from   Managing Data Science Effective strategies to manage data science projects and build a sustainable team

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781838826321
Length 290 pages
Edition 1st Edition
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Author (1):
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Kirill Dubovikov Kirill Dubovikov
Author Profile Icon Kirill Dubovikov
Kirill Dubovikov
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Table of Contents (18) Chapters Close

1. Section 1: What is Data Science?
2. What You Can Do with Data Science FREE CHAPTER 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

Exploring innovation management

Innovations are very chaotic in nature. Innovators call for experimentation, but they cannot predict the end results and struggle to define deadlines. These qualities of innovations make them hard to implement in a business environment, where clearly defined goals, strict deadlines, and finite budgets are the norm. Innovation management provides a set of techniques that bring order into a chaotic realm of innovations. The word management is associated with direct control, but this is not the case for innovations. Freedom is critical for every innovation. You won't get any positive results by trying to micromanage. Innovation management is about providing support and integrating innovations into existing businesses so that they deliver helpful results.

To understand the main ideas behind innovation management, we should understand the different...

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