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Principles of Data Science

You're reading from   Principles of Data Science A beginner's guide to essential math and coding skills for data fluency and machine learning

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781837636303
Length 326 pages
Edition 3rd Edition
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Author (1):
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Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Data Science Terminology 2. Chapter 2: Types of Data FREE CHAPTER 3. Chapter 3: The Five Steps of Data Science 4. Chapter 4: Basic Mathematics 5. Chapter 5: Impossible or Improbable – A Gentle Introduction to Probability 6. Chapter 6: Advanced Probability 7. Chapter 7: What Are the Chances? An Introduction to Statistics 8. Chapter 8: Advanced Statistics 9. Chapter 9: Communicating Data 10. Chapter 10: How to Tell if Your Toaster is Learning – Machine Learning Essentials 11. Chapter 11: Predictions Don’t Grow on Trees, or Do They? 12. Chapter 12: Introduction to Transfer Learning and Pre-Trained Models 13. Chapter 13: Mitigating Algorithmic Bias and Tackling Model and Data Drift 14. Chapter 14: AI Governance 15. Chapter 15: Navigating Real-World Data Science Case Studies in Action 16. Index 17. Other Books You May Enjoy

How do we measure statistics?

Once we have our sample, it’s time to quantify our results. Suppose we wish to generalize the happiness of our employees or we want to figure out whether salaries in the company are very different from person to person.

These are some common ways of measuring our results.

Measures of center

Measures of the center are how we define the middle, or center, of a dataset. We do this because sometimes we wish to make generalizations about data values. For example, perhaps we’re curious about what the average rainfall in Seattle is or what the median height of European males is. It’s a way to generalize a large set of data so that it’s easier to convey to someone.

A measure of center is a value in the middle of a dataset. This can mean different things to different people. Who’s to say where the middle of a dataset is? There are so many different ways of defining the center of data. Let’s take a look at a...

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