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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

Conducting Data Science Interviews

In this chapter, you will learn how to conduct an efficient data science interview. We will explore how a hiring process can be tied to real working experience so that the candidates you seek will clearly understand what kind of job they will need to do. The advice in this chapter will help you to get rid of unnecessary interviewing rituals and make the hiring process more efficient.

How long has it been since you went through a technical interview? Was it a pleasant experience? And if you got through it, can you judge the relevance of your interview questions to your day-to-day work? I bet that the experience was awful. The common complaints are that all interviews are stressful, and the questions are not relevant. Technical interviews are deeply flawed. We imagine that we are using a rational way of thought to pass candidates through a sieve...

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