Data Types and Characteristics of Data

Quantitative Data

Quantitative data is portrayed through numbers, meaning it can have a numerical value (like price) or be measured (like weight, distance, or height).
Two characteristics of quantitative data:
- Discrete - data that identifies data that can be counted and can only take on a certain number of values.
- Continuous - data that identifies data that can be measured and can use any value.

Qualitative data - data that can be arranged into groups or categories based on its qualities; also known as categorical data.
Two characteristics of qualitative data:
- Nominal data is information that has no natural order.
- Ordinal data is information that follows a natural order.

Qualitative data:
- Categories displayed in a chart are categorical or qualitative.
- Cannot be naturally totaled but often counted.
Quantitative Data:
- Sums, averages, and numbers shown in data and visuals.
- Mixed with categorical data to provide more meaning to the analysis.

What are the two highest-level types of data for a field?
Suppose a survey asks respondents for their level of education. This would be considered qualitative data with what characteristic?
If you ask people to tell you their favorite TV show, what type of data are you collecting?
When determining how much product was ordered over time, the quantity ordered is what high-level type of data?
When data can be measured, which characteristic of data is it?

Used to store any type of letter and/or number within the field.
Example: 35007-0023 is an alphanumeric value. The dash requires this field to be set as an alphanumeric data type.

Dates defined with a date data type can capture a date and/or time.
Needed to calculate other dates or information from a date (e.g., numbers of days between two days).
Can be used to pull information such as the month number, week number, or day of the week.

Number data type will not allow any text.
Has a variety of different attributes.
Each database system or data software will provide a variety of number data types.

The currency field data type is a number.
Can be a number data type formatted to be displayed as currency with a currency symbol and decimal places, or assigned a currency data type.
Defining numbers as currency enables the use of currency symbols for a particular geographical area.

A subset of the number data type commonly used for Yes/No, True/False, and On/Off responses represented by a number that is either 1 or 0.
Each system will have its own defined way of working with the Boolean data type.

When a data type isn’t as needed, it will need to be converted to the appropriate data type to meet requirements.
Examples:
- A field containing a date is not set as the date data type, and you want to calculate various dates.
- A field is displaying a number but is stored as text; to sum or count it, you will convert it to a number.

Why are field data types important to the data analyst?
Which field data type is the most versatile of all?
What feature controls what type of information is stored in a field?
Who defines the field-level data type in the source system, and what can an analyst do if it is incorrect for the type of analysis required?

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Assisted Lab 2: Understanding Data Types and Conversion
Applied Lab 4: Understanding Data Structure and Types and Using Basic Statements