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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

Calculating derived and reduced variables

In this section, we will talk about specialized variables that you can create that will help you as a data analyst. You will find that raw data, even clean raw data, can be difficult to interpret. When looking at a functional dataset that is actively being used by a data analyst, you will almost always find variables that were not present when the data was originally recorded. Instead, these variables were added later and contain some logic that allows them to create a new value based on those that were recorded.

Derived variables

Variables that use logic that relies on other variables are broadly called derived variables, though some also refer to them as calculated variables or fields. The idea is just that this variable was not observed but was generated based on data that was observed. If this definition seems a little vague, that’s because it is. There are as many derived variables as there are stars in the sky and they come...

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