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Hands-On Data Preprocessing in Python

You're reading from  Hands-On Data Preprocessing in Python

Product type Book
Published in Jan 2022
Publisher Packt
ISBN-13 9781801072137
Pages 602 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Roy Jafari Roy Jafari
Profile icon Roy Jafari
LinkedIn
Roy Jafari, Ph.D. is an assistant professor of business analytics at the University of Redlands. Roy has taught and developed college-level courses that cover data cleaning, decision making, data science, machine learning, and optimization. Roy's style of teaching is hands-on and he believes the best way to learn is to learn by doing. He uses active learning teaching philosophy and readers will get to experience active learning in this book. Roy believes that successful data preprocessing only happens when you are equipped with the most efficient tools, have an appropriate understanding of data analytic goals, are aware of data preprocessing steps, and can compare a variety of methods. This belief has shaped the structure of this book.
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Table of Contents (24) Chapters

Preface 1. Part 1:Technical Needs
2. Chapter 1: Review of the Core Modules of NumPy and Pandas 3. Chapter 2: Review of Another Core Module – Matplotlib 4. Chapter 3: Data – What Is It Really? 5. Chapter 4: Databases 6. Part 2: Analytic Goals
7. Chapter 5: Data Visualization 8. Chapter 6: Prediction 9. Chapter 7: Classification 10. Chapter 8: Clustering Analysis 11. Part 3: The Preprocessing
12. Chapter 9: Data Cleaning Level I – Cleaning Up the Table 13. Chapter 10: Data Cleaning Level II – Unpacking, Restructuring, and Reformulating the Table 14. Chapter 11: Data Cleaning Level III – Missing Values, Outliers, and Errors 15. Chapter 12: Data Fusion and Data Integration 16. Chapter 13: Data Reduction 17. Chapter 14: Data Transformation and Massaging 18. Part 4: Case Studies
19. Chapter 15: Case Study 1 – Mental Health in Tech 20. Chapter 16: Case Study 2 – Predicting COVID-19 Hospitalizations 21. Chapter 17: Case Study 3: United States Counties Clustering Analysis 22. Chapter 18: Summary, Practice Case Studies, and Conclusions 23. Other Books You May Enjoy

Summary

Congratulations on your excellent progress in this chapter. First, we learned the difference between data fusion and data integration before becoming familiar with six common data integration challenges. Then, through three comprehensive examples, we used the programming and analytic tools that we've picked up throughout this book to face these data integration challenges and preprocess the data sources so that we were able to meet the analytic goals.

In the next chapter, we will focus on another data preprocessing concept that is crucial, especially for algorithmic data analytics due to the limitations of computational resources: data reduction.

Before you start your journey on data reduction, take some time and try out the following exercises to solidify your learning.

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Authors (1)

Profile icon Roy Jafari
LinkedIn
Roy Jafari, Ph.D. is an assistant professor of business analytics at the University of Redlands. Roy has taught and developed college-level courses that cover data cleaning, decision making, data science, machine learning, and optimization. Roy's style of teaching is hands-on and he believes the best way to learn is to learn by doing. He uses active learning teaching philosophy and readers will get to experience active learning in this book. Roy believes that successful data preprocessing only happens when you are equipped with the most efficient tools, have an appropriate understanding of data analytic goals, are aware of data preprocessing steps, and can compare a variety of methods. This belief has shaped the structure of this book.
Read more
See other products by Roy Jafari

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