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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

Defining the elements of a technology stack

A technology stack is a set of tools that your team uses to deliver products and finish projects. When choosing technologies, you start by defining their goals and thoroughly documenting all the requirements. From there, you and your team can see what technologies will help you to reach the end goal.

Shaping a technology stack goes toe-to-toe with designing software architecture, so the engineers on your team should start by drafting a system design that will meet everyone's requirements. Software architecture is a wide and deeply technical topic, so we won't discuss it in depth in this chapter. Instead, we will present an overview of the necessary steps when it comes to choosing the best technologies for reaching specific goals. Let's get started:

  1. Collect the requirements and define the goals clearly.
  2. Choose a set of...
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