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CompTIA Data+: DAO-001 Certification Guide

You're reading from   CompTIA Data+: DAO-001 Certification Guide Complete coverage of the new CompTIA Data+ (DAO-001) exam to help you pass on the first attempt

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804616086
Length 370 pages
Edition 1st Edition
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Author (1):
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Cameron Dodd Cameron Dodd
Author Profile Icon Cameron Dodd
Cameron Dodd
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Table of Contents (24) Chapters Close

Preface 1. Part 1: Preparing Data
2. Chapter 1: Introduction to CompTIA Data+ FREE CHAPTER 3. Chapter 2: Data Structures, Types, and Formats 4. Chapter 3: Collecting Data 5. Chapter 4: Cleaning and Processing Data 6. Chapter 5: Data Wrangling and Manipulation 7. Part 2: Analyzing Data
8. Chapter 6: Types of Analytics 9. Chapter 7: Measures of Central Tendency and Dispersion 10. Chapter 8: Common Techniques in Descriptive Statistics 11. Chapter 9: Hypothesis Testing 12. Chapter 10: Introduction to Inferential Statistics 13. Part 3: Reporting Data
14. Chapter 11: Types of Reports 15. Chapter 12: Reporting Process 16. Chapter 13: Common Visualizations 17. Chapter 14: Data Governance 18. Chapter 15: Data Quality and Management 19. Part 4: Mock Exams
20. Chapter 16: Practice Exam One 21. Chapter 17: Practice Exam Two 22. Index 23. Other Books You May Enjoy

Data Wrangling and Manipulation

It is not an exaggeration to say that the majority of work done by the average data analyst revolves around preparing data for use. A large part of this is cleaning the data, as covered in the previous chapter, but it is more than just dealing with things that will cause errors or introduce bias. You will often have to get the data into a specific shape or format before you can use it. This step is often called data wrangling or manipulation. To be clear, when we use the word “manipulation,” we do not mean we are changing the outcome in any way; we are using it in the literal sense of handling and managing the data in a skillful way.

In this chapter, we will go over some of the most important skills in the data-wrangling process. We will talk about the different methods to combine datasets including different types of joins, blends, concatenation, and appending. Next, we will look at the addition of variables that add meaning, such...

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