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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 first covered what a data scientist is. Then, we looked at two examples that showed us whether a data scientist can work in isolation or needs a team. Next, we looked at the various skills and qualities that a data scientist, a data engineer, and a data science manager need to have. We also briefly explored when we need to ask for help from the development team.

Finally, we defined the key domains of a data science team, which are analysis, data, and software. In those domains, we defined project roles that will allow you to create a balanced and powerful team. We may solve simple projects with small teams where team members share responsibilities for different roles. But when the project's complexity grows, your organizational structure will need to scale with it. We also noted the importance of following the best practices of software engineering...

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