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

Finding variance and standard deviation

Variance and standard deviation are very popular. They are a little bit more complicated to perform by hand, not that you would ever perform them by hand if you didn’t have to for the exam, but they are a much better measure of how dispersed your data is. Instead of giving you a rough idea based on the range, these tell you the average distance of every point from your mean.

Variance

Variance is a measure of dispersion that looks at the squared deviation of a random variable from the mean of that variable. This equation looks a little scary, but we will break it down step by step:

=

It should be noted that this denominator (n-1) is used for samples. If you are using an entire population, then the denominator is just (). Let’s go over this briefly. is the sample variance, represents the value of each observation, represents the mean of the sample, and is the number of data points in your dataset. Let’s go ahead...