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

Discovering trends

Trend analysis, sometimes called time series analysis and projections, does exactly what it sounds like, analyze trends. Again, this topic is very broad because it is used in so many fields in so many ways. The general idea is that you are looking at a variable over time to see whether there are any patterns or whether you can predict what will happen in the immediate future. The further into the future you go, the less accurate the predictions become.

For example, you know your own weight in pounds. It is a decently safe bet to say that in 1 second, your weight may go up or down a pound because of measurement errors, but it probably won’t change. What about in a week? You could probably say your weight won’t change, but it could theoretically go up or down 5 pounds. A month? Give or take 15 pounds. A year? Give or take 50 pounds. A decade? Give or take 100 pounds. I think you see where this is going. The further into the future you get, the more...