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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

Thinking of projects as products

To benefit from the ideas in this chapter, you must think about your work as product development. While many companies deliver software products to the market, you deliver services. We can perceive services as a product; they also obey the laws of supply and demand, and there are markets for different types of services.

Like software products, you can decompose your activity into service features. Some of the aspects of your service that your team is good at will separate your department or company from your competitors, but you will likely find some of these aspects lagging behind other organizations that focus on this particular type of service. For example, a data science consulting company may shine in creating custom models. However, their user interfaces (UIs) will be worse than that of a specialized UI development company. These tradeoffs...

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