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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

Finding project ideas

Before implementing your first prototype, you will need to find promising ideas to work on. To create ideas, you can work from one of two starting points: business or data.

Finding ideas in business processes

If you are working with an already established business, the first and the most obvious way to find ideas is to ask management about their needs. Merging data science expertise with deep domain knowledge will allow you to find a match between their requests and your team's capabilities. However, your customers will rarely have a problem they know about. The most likely response you will get while using this strategy is, We know of this problem, and we are already working on the solution.

On...

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