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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

Approaching research projects

A research project is any project that gives you solutions to new, not well-known problems. Research projects aren't always about advancing science. If your team deals with a new kind of business domain, or a new type of machine learning library, these are also considered to be research projects. Discovering ways to apply data science to new business domains is also research. Almost every data science project includes a research subproject that takes care of the modeling process.

The first pitfall of research projects is the absence of scope. Every research project must have a clear scope, otherwise it won't be possible to finish it. It is also important to fix any external constraints for a research project. Your research budgets will grow as the scope's size increases, so limited budgets may also affect the depth and length of research...

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