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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? 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

Defining data science team roles

Data science teams need to deliver complex projects where system analysis, software engineering, data engineering, and data science are used to deliver the final solution. In this section, we will explore the main data science project roles. The project role depicts a set of related activities that can be performed by an expert. Role-expert is not strictly a one-to-one correspondence, as many experts have the expertise to handle multiple roles at once.

An average data science team will include a business analyst, a system analyst, a data scientist, a data engineer, and a data science team manager. More complex projects may also benefit from the participation of a software architect and backend/frontend development teams.

Here are the core responsibilities of each team role:

  • Project stakeholders: Represent people who are interested in the project...
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