From the course: Introduction to Data Science
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Distinguishing properties of data
From the course: Introduction to Data Science
Distinguishing properties of data
Exploratory data analysis, also known as EDA, involves determining the key properties of the data you have in front of you. Now, I'll go over the main properties you investigate when conducting EDA. Ask yourself these questions about each property. The first main property is known as granularity, which is what each record in the data represents. Consider asking how fine or coarse is the data. The next property is scope. The scope of the data set refers to the coverage of the data set in relation to what you're interested in analyzing. What does the data describe? Does the data cover the topic you're interested in? After that, there's temporality. This refers to how the data is situated in time and specifically to the date and time fields in the data set. When was the data collected? Finally, there's faithfulness. How accurately does a data describe the real world? Should you trust this data? For example, say you have data regarding shows and movies that were on Netflix from 2015-2021…
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