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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

Managing Innovation

In the Building and Sustaining a Team section of this book, we looked at how we can create a balanced team that can deliver data science solutions. Now, we will look at how we can find projects and problems that have real value. In this section of the book, we will look at data science management at a greater scale. We are moving on from team leadership to the area of data science project management. We will develop specific strategies and approaches so that we can find, manage, and deliver projects that are valuable for the business.

For most companies, data science and machine learning belong in the area of innovation. Unlike software development, these disciplines are terra incognita (unknown territory) for your customers and business stakeholders. If you approach data science projects like any other project, you will face many unexpected problems. The domain...

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