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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

Choosing between core- and project-specific technologies

Technology choices should help with project requirement realization, but it is also crucial to take your team's expertise and capabilities into account, as well as the constraints. For example, if your team consists entirely of Python developers, choosing Julia as a primary programming language may be a bad idea, even if the team sees it as a better fit for the project:

  • All of the team members will spend time learning a new language, practically destroying all productivity gains from using the technology.
  • The team's conclusions may be over-optimistic because of their lack of experience with the new technology.

Those two risks abate if your team pursues a growth mindset and gains new knowledge continuously, but they never vanish completely.

The core expertise in your team puts limits on what technologies you can...

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