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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

Understanding data security

In this section, we will discuss data security. Data is the most important resource for a data analyst. Without data, we would be out of a job. Not only is it important to have data, but it is important to make sure the data maintains its integrity. Data integrity is basically how valid, or accurate, the data is, and maintaining data integrity involves work on several different levels. For example, using the data to calculate a new variable does not impact the original data at all; the data itself hasn’t been touched and there is no impact on data integrity. However, changing or manipulating the data to show trends that are not there does impact data integrity. Incorrect use of the data means the data becomes useless because the sample will no longer reflect the population. Through malice or mistake, if anyone can access the data from anywhere at any time, then the data is at serious risk. Here, we will discuss how to keep the wrong people from messing...

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