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

This chapter covered how to clean and process data. First, we covered the difference between duplicate data and redundant data and how to deal with each. Then, we talked about the highly debated question of what to do with missing data, which covered the different types of missing data, different methods of deleting missing data, different types of imputation, and interpolation. Next, we went over common issues such as invalid data, specification mismatch, and data type validation. Then, we covered non-parametric data, what it is, and what that means for you. Finally, we discussed outliers and how to address them. This wraps up how to clean your data. In the next chapter, we will cover how to wrangle your data and get it into a shape you can use!