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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

Exploring data science team roles and their responsibilities

To complete a data science project, you will need a data scientist. Can a single expert lead a project? To answer this question, we can break down data science projects into stages and tasks that are, to some extent, present in all projects.

Before starting a project, you need an idea that will allow your client to achieve their goals and simplify their life. In business, you will look to improve key business processes within a company. Sometimes, the idea is already worked out, and you may start directly from implementation, but more often, your team will be the driver of the process. So, our ideal expert must be able to come up with an idea of a data science project that will provide value for the client.

Next, we will study two project examples to look at how simple projects can be handled with small teams or even...

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