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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

Seeking and building products

Over time, it is natural to have teams that are dedicated to the development of the most complex and popular parts of your reusable toolchain. If you have a reusable component that has stood the test of time in multiple projects and is backed up by an experienced team, it is worth considering turning it into a product.

Another sign that you should make products out of your projects is a high demand for some type of custom solution that you built. If every client asks you to build a customer support chatbot and you have already built tens of them, why not make a product to make everyone's lives easier?

If your company has no prior experience of delivering new products to the market, take this task seriously and be ready to transition into a new world. If you want to avoid mistakes, take the time to read the literature related to product management...

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