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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

Choosing a methodology that suits your project

Choosing a project management methodology can become a captivating and complex task. You can spend a lot of time thinking about how one approach will support your processes better than another, and what limitations it will have. Try not to spend much of your time on methodological considerations. It is much more important to choose something and stick with it unless it is clearly harming your project. To simplify this process, we will explore some simple guidelines when it comes to choosing a management approach.

Creating disruptive innovation

If you create a solution that should disrupt the market, the only thing that matters is the efficiency of your methodology. You won&apos...

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