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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

Exploring machine learning

Now that you understand the general flow of thought on how to define learning processes using mathematics and statistics, we can explore the inner workings of machine learning. Machine learning studies algorithms and statistical models that are able to learn and perform specific tasks without explicit instruction. As every software development manager should have some expertise in computer programming, the data science project manager should understand machine learning. Grasping the underlying concepts between any machine learning algorithm will allow you to understand better the limitations and requirements for your project. It will ease communication and improve understanding between you and the data scientists on your team. Knowledge of basic machine learning terminology will make you speak in the language of data science.

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