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)
Part 1: Preparing Data
Part 2: Analyzing Data
Part 3: Reporting Data
Part 4: Mock Exams

Recoding variables

In this section, we will discuss all of the different ways you can recode variables to turn them into a format you can use. Recoded variables, as mentioned when we talked about derived variables, are a kind of derived variable. Instead of the focus being on summarizing data, the idea is to create a direct translation of a variable into a different format. This is how you turn quantitative variables into qualitative variables and vice versa. Recoding can try to make data easier to understand, but it is most often used because a certain analysis requires data to be in a specific format to run.

Recoding numbers into categories

When it comes to creating a category based on a number, ranges are the most common approach. Effectively, you are saying if the value of X is between A and B, it falls into this group, and if X is between B and C, it falls into this other group. Where specifically you make the cut-offs to set the ranges can get very complicated and is a...