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

Understanding data security

In this section, we will discuss data security. Data is the most important resource for a data analyst. Without data, we would be out of a job. Not only is it important to have data, but it is important to make sure the data maintains its integrity. Data integrity is basically how valid, or accurate, the data is, and maintaining data integrity involves work on several different levels. For example, using the data to calculate a new variable does not impact the original data at all; the data itself hasn’t been touched and there is no impact on data integrity. However, changing or manipulating the data to show trends that are not there does impact data integrity. Incorrect use of the data means the data becomes useless because the sample will no longer reflect the population. Through malice or mistake, if anyone can access the data from anywhere at any time, then the data is at serious risk. Here, we will discuss how to keep the wrong people from messing...