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 type I and type II errors

When we are talking about errors in hypothesis testing, we are not talking about typos, or even mistakes made with the study to gather data. We are talking about coming up with the wrong answer. Earlier, we went over there only being two outcomes to a hypothesis test; either you accept and reject or you accept and reject . Those are the only two possible outcomes. We also discussed that this is based on probability and there will always be a small chance that you choose incorrectly. Even a 95% chance of being right means you are wrong 1 in 20 times. In other words, both of the two possible outcomes have an error associated with them. These errors are called type I and type II errors.

Type I error

Type I error is when you incorrectly accept and reject . Your p-value was smaller than your alpha, so you said there was a statistically significant difference between the two groups, but it turns out they were really just the same thing. Going...