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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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Toc

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

Conclusion

First of all, thank you for reading this book! I hope that the material was helpful in presenting the overall management approach to data science projects. Data science management is a multidimensional topic that requires a manager to show technical, organizational, and strategic proficiency so that they can execute a data science strategy inside any organization.

First, the data science manager needs to understand what data science can do, as well as its technical limitations. Without an understanding of the basic concepts, it is extremely easy to misunderstand your colleagues or provide over-promising project results to the customer. The What is Data Science section of this book described the basics of machine learning and deep learning, including explanations behind the mathematical concepts that comprise machine learning algorithms. We explored the world of technology...

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