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Data Science for Decision Makers

You're reading from   Data Science for Decision Makers Enhance your leadership skills with data science and AI expertise

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781837637294
Length 270 pages
Edition 1st Edition
Languages
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Author (1):
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Jon Howells Jon Howells
Author Profile Icon Jon Howells
Jon Howells
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Understanding Data Science and Its Foundations
2. Chapter 1: Introducing Data Science FREE CHAPTER 3. Chapter 2: Characterizing and Collecting Data 4. Chapter 3: Exploratory Data Analysis 5. Chapter 4: The Significance of Significance 6. Chapter 5: Understanding Regression 7. Part 2: Machine Learning – Concepts, Applications, and Pitfalls
8. Chapter 6: Introducing Machine Learning 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Unsupervised Machine Learning 11. Chapter 9: Interpreting and Evaluating Machine Learning Models 12. Chapter 10: Common Pitfalls in Machine Learning 13. Part 3: Leading Successful Data Science Projects and Teams
14. Chapter 11: The Structure of a Data Science Project 15. Chapter 12: The Data Science Team 16. Chapter 13: Managing the Data Science Team 17. Chapter 14: Continuing Your Journey as a Data Science Leader 18. Index 19. Other Books You May Enjoy

Day-to-day management of a data science team

Leading a data science team is a unique challenge that requires balancing innovation and pragmatism. To drive impactful data science, you must foster a culture of experimentation while ensuring that the team’s efforts create business value.

Enabling rapid experimentation and innovation

Successful data science teams embrace rapid experimentation and learn from failures. As a leader, encourage risk-taking and celebrate lessons learned from unsuccessful endeavors. Provide access to powerful tools and platforms, such as cloud computing services, to accelerate the experimentation process. Meta is known for its “move fast” culture, which encourages rapid experimentation and iteration in all aspects of the company, including artificial intelligence and machine learning.

Managing inherent uncertainty

Data science projects are inherently uncertain, with outcomes often unclear at the outset. Manage this ambiguity by...

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