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

Validating quality

There are many ways to check the quality of your data, but there are a few forms that are more common than others. Let’s take a look at the most popular, which are also the ones you need to know for the exam. These include the following:

  • Cross-validation
  • Sample/spot check
  • Reasonable expectations
  • Data profiling
  • Data audits

Some of these are pretty self-explanatory, but let’s go into a little more detail for each.


Cross-validation is a statistical analysis that checks to see whether the results of a different analysis can be generalized. This analysis has many different uses. It can check data model effectiveness, specifically if you are checking for overfitting. Often, it is used to figure out what the hyperparameters should be for your model, and is a great tool for reducing test error. Cross-validation is a useful tool that can be applied in several different ways to check the quality and function...