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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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Toc

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

Section 2: Building and Sustaining a Team

Data science is an innovation for most organizations. However, every innovation requires deep and careful thinking – not all ideas are equally good, and not all of them have the necessary resources for implementation. This chapter helps you to identify the best ideas and strip them down to the minimum valuable product.

Another important consideration is how successfully you can sell your idea to all stakeholders who might benefit from it.

Merging a knowledge of modern data analysis algorithms and business domain expertise is a necessary step for every project. This chapter outlines the importance of using a scientific approach in business. It helps you to answer the following questions: how can you find an efficient data science application for your business? What are business metrics and technical metrics and how should we define...

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