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

Learning about p-value and alpha

Let’s talk about the results of an analysis and how those translate to accepting and rejecting hypotheses. First, it should be noted that many different analyses can be used in hypothesis testing, and a few might give their results in different terms. However, the majority of tests that are used for hypothesis testing produce something called a p-value. This value is either above or below a cutoff score, set by the alpha, and that determines which hypothesis you accept and which you reject. Let’s look at these topics in more detail.

p-value

A p-value is a number produced by many analyses used in hypothesis testing. A p-value can, theoretically, be any value between 0 and 1. This may sound like a small range, but with decimals, there is literally an infinite number of values between 0 and 1. If you get into the technical definition of the p-value, it gets needlessly complicated very quickly. You can think of the p-value as the probability...