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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

Collecting Data

In this chapter, we will discuss sources of data. Sometimes data is not already nicely and neatly stored for you, and you will have to reach out to collect your own. Here, we will discuss the most common sources of information and a couple of systems that will pull or store data automatically. You will also learn how to make your queries more efficient if you are accessing a database. These resources and techniques are crucial, not only to passing the CompTIA Data+ certification exam but also to the everyday life of a data analyst.

In this chapter, we’re going to cover the following main topics:

  • Utilizing public sources of data
  • Collecting your own data
  • Differentiating Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT)
  • Understanding online transactional processing (OLTP) and online analytical processing (OLAP)
  • Optimizing query structure
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