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)
Part 1: Preparing Data
Part 2: Analyzing Data
Part 3: Reporting Data
Part 4: Mock Exams

Cleaning and Processing Data

On rare occasions, you may receive data that is already clean, neat, and ready to use, but having an immaculate dataset just handed to you is the exception, not the rule. More often than not, while working as a data analyst, the datasets you receive will be messy, incomplete, and completely unusable without a little work. Trying to use jumbled data will only give you jumbled results. This chapter covers the most common issues you will come across and a few approaches to dealing with them.

Here, we will discuss the difference between duplicate data and redundant data, as well as what to do about it. Then, we will discuss why missing data is an issue and the different approaches you can take to deal with it. Briefly, we will cover invalid data, mismatched data, and data type validation. After that, we will discuss non-parametric data, what it is, and how to approach it. Finally, we will discuss outliers or data points that don’t seem to fit in with...