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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

Understanding the data you have

Once you have gone through the process of collecting and storing data, it can be tempting to dive straight into the more interesting and exciting work of training machine learning models or building dashboards to present to your customers or stakeholders.

However, an important stage before model training or presenting results is to explore and understand the data you have, as well as its main characteristics, patterns and trends in the data, and potential anomalies or outliers.

EDA is a fundamental step in the data analysis process that involves systematically examining datasets to understand their main characteristics, identify patterns and trends, and uncover potential anomalies or outliers. EDA typically precedes more formal statistical or machine learning modeling, and its primary goal is to provide insights and context that will inform further analysis and model development.

The importance of EDA cannot be overstated. It not only helps...

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