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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?
2. What You Can Do with Data Science FREE CHAPTER 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

Designing good interviews

How can we make interviews more relevant and time-intensive? An ideal interview would be testing in a real working environment for a couple of months. While there are several companies that can afford to operate without interviews, using paid probation periods instead, this is a very costly hiring strategy that not every business can afford. A good interview should serve as a substitute for real working experience. It should not be a test of a person's skill, but a test for a person's ability to perform a specific job well. If an ideal test for a candidate is a probation period, then the worst kind is a whiteboard interview (unless you are interviewing a computer science lecturer). To design a great interview, bring it as close as possible to your working process and to the issues you solve on a daily basis.

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