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

Understanding t-tests

In this section, we will cover the Student’s t-test. This is a staple analysis, and some form of it is often used in hypothesis testing. Because of how useful it is, how often it comes up, and how relatively simple it is, a t-test is often the first inferential analysis data analysts learn. There are several different forms of this, but the main three are as follows:

  • Independent t-test
  • Dependent t-test
  • One-sample t-test

These are pretty much what they sound like. An independent t-test, sometimes called an unpaired t-test, compares two groups that are independent of each other; the samples include different people or observations, gathered at different times, under different conditions. A dependent t-test, sometimes called a paired t-test, means that the two groups you are comparing are inherently related. If you collect data on a group of people at the start of the year and then collect data on the same group of people a year later...