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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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Toc

Table of Contents (18) Chapters Close

1. Section 1: What is Data Science? FREE CHAPTER
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

Summary

In this chapter, we have explored innovations and learned about how to manage them. Good innovation management requires us to carefully plan our own activity and use different viewpoints such as sales, marketing, and technical leadership to match our ideas with market requirements.

First, we described how each viewpoint contributes to effective innovation management. Next, looked at the complexities of integrating innovations into big organizations and start-ups. Data science is an innovative activity for most organizations, and we have defined several strategies that we can use to find project ideas and make them work.

We have also looked at three case studies, each related to different topic related to innovation management. The first one, following the innovation cycle at MedVision, showed us how an innovation cycle can be applied in a real-world scenario. The second...

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