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CompTIA Data+: DAO-001 Certification Guide

CompTIA Data+: DAO-001 Certification Guide

By : Cameron Dodd
5 (11)
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CompTIA Data+: DAO-001 Certification Guide

CompTIA Data+: DAO-001 Certification Guide

5 (11)
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)
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1
Part 1: Preparing Data
7
Part 2: Analyzing Data
13
Part 3: Reporting Data
19
Part 4: Mock Exams

Understanding quality control

Quality control is the process of testing data to ensure data integrity. Here, we will go over when to perform quality control checks and what sorts of things these checks are trying to find. Remember, bad data leads to bad results, and bad results are worse than no results because they are actively misleading. It is important that your data is as accurate as possible, and to do that, you need quality control.

When to check for quality

While you will probably automate as much of the quality control practices as you can, there are times beyond the routine when it is important to check the quality of your data. You may use different quality control techniques in different instances, but in general, you need to check your data any time there is a major change. Lots of things may qualify as a major change, but the most common are as follows:

  • Data acquisition

Data acquisition is whenever you get new data. This doesn’t necessarily...

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