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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?
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

Section 1: What is Data Science?

Before diving into the management issues of building systems around machine learning algorithms, we need to explore the topic of data science itself. What are the main concepts behind data science and machine learning? How do you build and test a model? What are the common pitfalls in this process? What kinds of models are there? What tasks can we solve using machine learning?

This section contains the following chapters:

  • Chapter 1, What You Can Do with Data Science
  • Chapter 2, Testing Your Models
  • Chapter 3, Understanding AI
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