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

Dealing with missing data

Missing or incomplete data is a problem every data analyst will have to face at one time or another. Data can be missing for any number of reasons. Maybe someone just didn’t enter the data, maybe it’s a survey and the person didn’t answer the question, or a measurement couldn’t be taken for whatever reason. No matter the reason, holes in your dataset happen all the time, and it is something that needs to be addressed.

From a data analytics point of view, the biggest problem is that most analyses won’t run with null values in the data. You get an error message, and you can’t run the code until you have done something about all the gaps. From a statistical point of view, it is a little more complicated. Removing data reduces the statistical power of the analysis, and it can even drop the number of observations below what is required for a specific analysis. Perhaps the biggest problem is that sometimes what is missing...