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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

The importance of automated testing

Automated testing is considered to be mandatory in software engineering projects. Slight changes in software code can introduce unintended bugs in other parts, so it is important to check that everything works as intended as frequently as possible. Automated tests that are written in a programming language allow testing the system as many times as you like. The principle of CI advises running tests each time a change in code is pushed to a version control system. A multitude of testing frameworks exists for all major programming languages. Using them, developers can create automated tests for the backend and frontend parts of their product. Large software projects can include thousands of automated tests that are run each time someone changes the code. Tests can consume significant resources and require a lot of time for completion. To solve...

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