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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 FREE CHAPTER 2. Chapter 2: Types of Data 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

Exploring the data

The process of exploring data is not always straightforward and can involve a variety of approaches and techniques. Some common tasks that are involved in data exploration include recognizing different types of data, transforming data types, and using code to systematically improve the quality of the entire dataset. These tasks can be accomplished using tools such as the pandas Python package, which is commonly used for data manipulation and analysis.

There are a few basic questions that you should consider when exploring a new dataset. These questions can help you to get a sense of the data and guide your analysis. The three basic questions are presented here:

  • What are the types of data that are present in the dataset?
  • What are the characteristics and patterns of the data?
  • How is the data organized, and what transformations might be necessary to make it more usable?

By answering these questions and exploring your data thoroughly, you can...

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