Get started with data
A dataset is a collection of data that is organized in rows and columns, often referred to as a table. Rows are also known as records, and columns as fields. Data points make up the fields in a dataset that usually comprise different types of data:
Temporal data - Temporal data are dates held against an event, such as when an order was placed, or a package was shipped. Dates have a natural order, and we know that 12/24/2021 comes before 12/25/2021, and after 12/23/2021. Because of this natural order, it's easy to measure values over time.
String data - A text value is also referred to as character or string data, and is often classified as categorical data. This is because it describes categories of records within the dataset. For example, the name of an employee would be stored as string data.
Numeric data - Numeric data, or numbers, can be classed as either continuous or discrete. Continuous data is something we measure on a scale, and discrete data are values that are counted rather than measured. Depending on the values in a numerical column, this type can be used as a category.
It's important that you understand the data in your dataset before analyzing it. You can then choose the correct analytical functions to apply to the types of data you're working with.
In this video, you'll see an example of a dataset and how the different types of date, string, and numeric fields combine into rows and columns. You'll learn how analysis can be done on a dataset to understand patterns within the data: