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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 AI

You now have a good understanding of what data science can do and how we can check whether it works. We have covered the main domains of data science, including machine learning and deep learning, but still, the inner workings of the algorithms are difficult to discern through the fog. In this chapter, we will look at algorithms. You will get an intuitive understanding of how the learning process is defined using mathematics and statistics. Deep neural networks won't be so mystical anymore, and common machine learning jargon will not scare you but provide understanding and ideas to complete the ever-growing list of potential projects.

You are not the only one who will benefit from reading this chapter. Your new knowledge will streamline communication with colleagues, making meetings short and purposeful and teamwork more efficient. We will start at the heart...

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