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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

Applications of UL

UL, as we’ve discussed, is a type of ML that identifies patterns in data without the need for explicit supervision. It’s like a detective who arrives at a crime scene with no witnesses but must still piece together the story from the available evidence. But where does this kind of “detective work” find its application in the business world? Let’s explore.

Market segmentation

One of the most common applications of UL is in market segmentation. Businesses with a diverse customer base use clustering algorithms to group customers based on their behavior, demographics, and purchase history. This allows them to tailor their marketing strategies to each group, maximizing engagement and conversion rates.

Consider a global retail brand with millions of customers. They could use UL to segment their customers into groups, such as “young professionals,” “parents,” or “retirees,” each with distinct...

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