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

Summary

In this chapter, we talked all about hypothesis testing. We covered what it was, as well as a quick guide to the process of hypothesis testing. Next, we discussed a null hypothesis, an alternative hypothesis, and the difference between the two. Then you learned how the p-value is used to determine which hypothesis you accept and reject when compared to alpha. This led to type I and type II errors – what they are as well as how they interact with alpha. Finally, we went over what makes a good question for hypothesis testing and how to deal with a question that is less than ideal.

This has been a lot of theory in one chapter, but these are concepts that you must thoroughly understand, not only for the exam but also to perform any hypothesis testing as a data analyst. In the next chapter, we will cover some of the analyses you can use to perform a hypothesis test!