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

Differentiating ETL and ELT

You don’t always have to collect your data manually. Some programs will automatically pull data from a source, prepare it for use, and move it to a new location, usually your local environment. The code to automate this process is called a data pipeline. There are many kinds of data pipelines, and each will need to be tuned to which data you are pulling and what you need to do with it. While more complicated pipelines can automate entire modeling and reporting processes, the exam focuses on two types, and both types have the same three steps:

  1. Extraction
  2. Transformation
  3. Loading

Extraction is the step of pulling the data from the original source. The source can be a database you own, an outside database, or even an automated web scraping system—it doesn’t matter. Extraction is picking up the information from wherever it was originally stored. This is similar to the process you would do to manually collect it yourself...