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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 innovations in a big company

Most companies already know about the transformative power of data science. No one wants to fall out of competition, so companies create internal data science and R&D departments. Working in this environment is challenging because you are seen as the driving force of innovation by some and as a disturbing annoyance by others. Innovations often lead to significant changes in the existing business processes of the company, and many will not want to see these changes implemented.

Bringing disruptive innovations to existing businesses is a tricky process; to increase your chances of success, make sure you have the following:

  • Organizational power: You will need direct or indirect organizational power to implement changes.
  • A migration plan: You will need to have a complete vision of as-is (before integration) and to-be (after integration) business...
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