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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

Understanding innovations

Before we discuss failures in innovations, we need to understand what innovation is. The dictionary definition of innovation states that it is something new or something that's introduced in a different manner; an introduction of new things or methods. Every innovation is temporary. Edison's light bulb was once innovatory, as were cars. Successful innovations invade our daily lives like a tempest, change them forever, and then become mundane. Failed innovations fade and become forgotten.

If we look at history, the creation of technology does not guarantee success. Automobiles first appeared in 1886, but cars only really started changing our lives 27 years later, when the Ford Motor Company built the first moving assembly line in 1913. If you define innovation in terms of an inventor's success, the invention of the first car may be a huge...

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