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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

Writing the right questions

Here, we will go over what makes a good question when it comes to hypothesis testing. This question is called many different things, depending on the field: business question, research question, or even just question. However, these are all the same thing. The question is the reason for the hypothesis testing! This question is what you are trying to answer.

Important note

The specifics of what makes a good question might change a little based on the field. For academia, you may want a question that will take several studies and a dissertation to answer, but for this book, we are focusing on the common role of data analysts working in the industry.

The parts of a good question

A good question has only two parts:

  • What two groups you want compared
  • The metric you want to use to compare those groups

Let’s think of an example. Does product X sell more per week than product Y?

We want to compare product X to product Y &...

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