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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

Communicating Data

This chapter deals with the different ways of communicating results from our analysis. Here, we will look at different presentation styles as well as visualization techniques. The point of this chapter is to take our results and be able to explain them in a coherent, intelligible way so that anyone, whether they are data savvy or not, can understand and use our results.

Much of what we will discuss will be how to create effective graphs through labels, keys, colors, and more. We will also look at more advanced visualization techniques, such as parallel coordinate plots.

In this chapter, we will look into the following topics:

  • Identifying effective and ineffective visualizations
  • Recognizing when charts are attempting to “trick” the audience
  • Being able to identify causation versus correlation
  • Constructing appealing visuals that offer valuable insight
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