Data is only valuable if properly understood. Before crafting visualizations, we need to take stock of what we have, as data come in all shapes and sizes. This unit will show you commonly encountered data types and how to deal with them.
Completing this unit should take you approximately 2 hours.
Watch the video to see how to measure dependent variables and different types of variables. In an experiment or study, a dependent variable is the condition or concept being studied. A control variable is a condition that remains the same throughout the study. How can we classify measurements of variables? In this video, you will cover four basic scales of measurement. Pay attention to the definition of each scale, examples, and how or why the scale is used. Think of examples from your own experiences to make a connection to each scale and its use.
When a data set is missing data, full of mistakes, or in a rough form, data scientists call it "dirty data". Data analysis software will sometimes return an error and not analyze dirty data. In other cases, the software will run an analysis, but the dirty data will bias results. This video will show you how data can become dirty and some ways to clean it. Think about data you have worked with in the past. What errors were present in the data sets?
Big data, "data-driven", and other buzzwords are everywhere in modern industry. What do these terms really mean? These five videos define these terms and show how they relate to data visualization. You may want to take notes on these terms.
Take this assessment to see how well you understood this unit.