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


This chapter has covered a lot of information from some pretty diverse topics. First, we talked about public sources of data including public databases, open sources, APIs, and web services, as well as the pros and cons of using each. Then, we talked about the different ways to collect your own data, including web scraping, surveying—especially the different types of survey questions and survey bias—and observations. Then, we covered the difference between ETL and ELT, as well as a full load and a delta load, and why it is important. Next, we briefly covered OLTP and OLAP and how they are used to collect and process transactional data. Finally, we wrapped up the chapter by covering ways to optimize query structures, such as filtering, subsets, indexing, sorting, parameterization, temporary tables, subqueries, and execution plans. Whew! There sure are a lot of ways to collect data. In the next chapter, we will go over what to do with it once you have it!