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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

Defining AI

Media and news use AI as a substitute buzzword for any technology related to data analysis. In fact, AI is a sub-field of computer science and mathematics. It all started in the 1950s, when several researchers started asking whether computers can learn, think, and reason. 70 years later, we still do not know the answer. However, we have made significant progress in a specific kind of AI that solves thoroughly specified narrow tasks: weak AI.

Science fiction novels tell about machines that can reason and think like humans. In scientific language, they are described as strong AI. Strong AI can think like a human, and its intellectual abilities may be much more advanced. The creation of strong AI remains the main long-term dream of the scientific community. However, practical applications are all about weak AI. While strong AI tries to solve the problem of general intelligence...

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