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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

Introduction to machine learning

Machine learning is by far the most important tool of a data scientist. It allows us to create algorithms that discover patterns in data with thousands of variables. We will now explore different types and capabilities of machine learning algorithms.

Machine learning is a scientific field that studies algorithms that can learn to perform tasks without specific instructions, relying on patterns discovered in data. For example, we can use algorithms to predict the likelihood of having a disease or assess the risk of failure in complex manufacturing equipment. Every machine learning algorithm follows a simple formula. In the following diagram, you can see a high-level decision process that is based on a machine learning algorithm. Each machine learning model consumes data to produce information that can support human decisions or fully automate them...

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