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Managing Data Science

You're reading from  Managing Data Science

Product type Book
Published in Nov 2019
Publisher Packt
ISBN-13 9781838826321
Pages 290 pages
Edition 1st Edition
Languages
Author (1):
Kirill Dubovikov Kirill Dubovikov
Profile icon Kirill Dubovikov
Toc

Table of Contents (18) Chapters close

1. Section 1: What is Data Science?
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

Common flaws of technical interviews

If you have a software engineering background, how often have interviewers asked you to reverse a binary tree on a whiteboard, or to find a maximum subarray sum? If you come from a data science background, how many central limit theorem proofs did you lay out on a piece of paper or a whiteboard? If you are a team leader, have you asked such questions yourself? I am not implying that those questions are bad, but quite the opposite. Knowledge of core computer science algorithms and the ability to derive proofs may be important for some jobs. But for what purpose do we ask those questions? What do we want to know about the person on the other side of the table? For most companies, the ability to give answers to those questions is not relevant at all. What is the reason for asking them? Well, because stereotypical programmers must know algorithms...

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