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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

Parsing your data

In this section, we will talk about what parsing is and some common ways it is used. Sometimes you will receive data in a format that is not readily usable. Whether you are pulling data from a website, working with JSON files, or have big chunks of text, you will need to parse your data. There are many different parsers that you can use, depending on what you need to parse, but the general idea is that you are breaking a single large piece of data into several smaller pieces of data that can be easily identified and processed.

Natural Language Processing (NLP) is a field of data analytics that specializes in analyzing, you guessed it, language. Spoken or written, NLP is trying to translate common speech into actionable data. Parsing is necessary for even basic NLP.

Important note

In reference to NLP, parsing is called tokenization because it is breaking up the text into words, and each becomes its own object or token.

Let’s consider an example...

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