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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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Toc

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

Summary

Probability as a field works to explain our random and chaotic world. Using the basic laws of probability, we can model real-life events that involve randomness. We can use random variables to represent values that may take on several values, and we can use the probability mass or density functions to compare product lines or look at the test results.

We have seen some of the more complicated uses of probability in prediction. Using random variables and Bayes’ theorem are excellent ways to assign probabilities to real-life situations.

The next two chapters focus on statistical thinking. Like probability, these chapters will use mathematical formulas to model real-world events. The main difference, however, will be the terminology we use to describe the world and the way we model different types of events. In these upcoming chapters, we will attempt to model entire populations of data points based solely on a sample.

We will revisit many concepts in probability...

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