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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

Introduction to trend lines

By the end of this section, you’ll have a firm grasp of trend lines, the foundation of regression analysis.

Let’s begin with a practical example. Suppose you own an e-commerce store and have been recording your daily sales for the past few months. With a list of numbers at hand, you’re curious about any patterns in the data that could inform your business decisions. This is where trend lines come into play.

A trend line is a line that represents the general direction or pattern in a dataset. It allows us to visualize the relationship between data points and helps us make predictions about future values. In simple terms, it connects the dots in a way that best illustrates the overall trend.

Returning to our e-commerce store scenario, imagine plotting your daily sales on a graph, with days on the horizontal axis and sales on the vertical axis. Each day’s sales become a data point on the graph. Your goal is to draw a line...

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