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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

Introducing values and ethics into the interview

We often look at interviews from a one-sided perspective. We want to find a reliable team member who can do the job well. It is easy to forget that bad interviews can scare good candidates. More so, a constant flow of potential candidates make judgments about your company through the interviews. Bad interviews lead to bad publicity. The key to effective and smooth interviews is to think about the candidate experience.

If you have successfully defined honest requirements for the candidate, the next step is to think about the interview experience as a whole. To see a new teammate in the best light, the interview should look like a real working process, not like a graduate examination.

Consider how most technical interviews are conducted:

  • You go through a resume filter. The best people have PhDs and at least 5 years of (questionable...
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