Book Image

CompTIA Data+: DAO-001 Certification Guide

By : Cameron Dodd
Book Image

CompTIA Data+: DAO-001 Certification Guide

By: Cameron Dodd

Overview of this book

The CompTIA Data+ certification exam not only helps validate a skill set required to enter one of the fastest-growing fields in the world, but also is starting to standardize the language and concepts within the field. However, there’s a lot of conflicting information and a lack of existing resources about the topics covered in this exam, and even professionals working in data analytics may need a study guide to help them pass on their first attempt. The CompTIA Data + (DAO-001) Certification Guide will give you a solid understanding of how to prepare, analyze, and report data for better insights. You’ll get an introduction to Data+ certification exam format to begin with, and then quickly dive into preparing data. You'll learn about collecting, cleaning, and processing data along with data wrangling and manipulation. As you progress, you’ll cover data analysis topics such as types of analysis, common techniques, hypothesis techniques, and statistical analysis, before tackling data reporting, common visualizations, and data governance. All the knowledge you've gained throughout the book will be tested with the mock tests that appear in the final chapters. By the end of this book, you’ll be ready to pass the Data+ exam with confidence and take the next step in your career.
Table of Contents (24 chapters)
1
Part 1: Preparing Data
7
Part 2: Analyzing Data
13
Part 3: Reporting Data
19
Part 4: Mock Exams

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...