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Data Literacy

This guide was created to support the data literacy efforts of faculty, staff, and students at Tulane Libraries. If you have any suggestions for this guide, reach out to

Data Types and Formats

Qualitative and Quantitative Data Explained

Since data can take various forms, understanding the differences and intersections of qualitative and quantitative data can help you produce data-informed insights. 

Qualitative data are data that have no numerical value but rather represent categories or concepts. For example, someone's affiliation to the university (student, staff, faculty) would be considered qualitative information. Though mathematical calculations cannot be used on qualitative data, descriptive statistics such as frequency can describe qualitative data. Nominal data and ordinal data are the two qualitative data types.

Nominal Data

Nominal data are data that represent categories and have no quantitative (numeric) value or natural ordering. For example, someone's nationality would be considered nominal data: American, British, French, etc. This data type can be described statistically in terms of frequency. Other examples of  nominal data include hair color, religion, sexual orientation, college major, and preferred cell phone carrier.

Ordinal Data

Ordinal data are data that can be categorized and has a natural ordering but it has no quantitative (numeric) value. For example, someone's final place in a race would be considered ordinal data: first, second, third, etc. This data type can be described statistically in terms of frequency. Other examples of ordinal data include academic grades (A,B,C,D,F), education degree level (Bachelor's, Master's, Doctoral), and satisfaction rating (extremely dissatisfied, dissatisfied, neutral, satisfied, extremely satisfied). 

Qualitative data are data that have numerical value and can be processed using statistical methods. For example, someone's shoe size would be considered qualitative information. Qualitative data can be further divided into discrete data, continuous interval data, and continuous ratio data.

Discrete Data

Discrete data are data that has quantitative (numeric) value and can be counted, not measured. For example, the total number of cars parked on campus would be considered discrete data. This data type can be described statistically in aggregate -- as a single number, for example, there may be 50 cars parked on campus. This aggregate number can be subdivided based on a categorical feature -- there are 10 blue cars, 30 red cars, and 10 green cars. You can run more advanced statistical tests on discrete data if you observe the data over time. With this longitudinal view, you may ask the average cars parked on campus over a given temporal period (day, week, etc.). Other examples of discrete data include the number of students in a classroom, the number of computers in a given space, and the number of light fixtures in a commercial property.

Continuous Data

Continuous data are data that have quantitative (numeric) value but are measured instead of counted. For example, someone's height would be considered continuous data. When continuous data is observed and collected in a dataset, statistical procedures can be used on the determine the mean, mode, range, standard deviation, and other statistical characteristics of the data. Other examples of continuous data include temperature in a given space, time taken to complete a task, and length of a film. Continuous data can be converted without changing the value itself --> 3 feet is the same as 1 yard.

Continuous data can also be further divided into interval and ratio data. The difference between these types is that interval data can be represented by values less than zero (temperature, for example) while ratio data cannot. Temperature is an example of interval data whereas height is an example of ratio data.


Data Type     

Description Examples
Nominal can be categorized and have no quantitative (numeric) value or natural ordering gender categories, hair color, computer manufacturer
Ordinal can be categorized and has a natural ordering but it has no quantitative (numeric) value age, placement in a competition, satisfaction rating
Discrete has quantitative (numeric) value and can be counted, not measured. number of cars on campus, products sold in a month, number of players on a team
Continuous have quantitative (numeric) value but is measured instead of counted length of a film, body temperature, weight of a flamingo

Python is a powerful programming language used for data analysis, web development, application creation, and other technology/data heavy tasks. Data in python can take the following forms (list not exhaustive):

Basic Data Types

Integer - numeric type presented as a whole number

Float - numeric type presented as a decimal number

String - text type including single characters

Boolean - binary type

Data Structures

List - 0 or more items stored as one object

Dictionary - 0 or more items mapped to one another in a key-attribute structure

Set - 0 or more items stored as one object, items in a set cannot be changed but new items can be added

Tuple - 0 or more items stored as one object, items in a tuple cannot be changed and no new items can be added


Data Frames (used in the pandas library) - structured data presented as rows and columns (similar to a spreadsheet or csv file). To learn more about the pandas dictionary, visit the following link: Pandas DataFrames (



R is another programming language used for data-intensive tasks. Data in R can take the following forms referred to as R-objects (list not exhaustive):

Basic Data Types

Logical - binary data type (TRUE or FALSE)

Numeric - numerals inclusive of numerals with decimals

Integer - numerals exclusive of numbers with decimals (must include capital "L" after number to store value as an integer)

Complex - imaginary numerals, lowercase "i" is used to denote an imaginary number

Character - text type used for single characters and strings

Data Structures

Data structures contain 0 or more data instances.

Vectors - store multiple variables as one object

Lists - store multiple variables, including vectors, as one object

Matrices - store multiple variables in a two dimensional structure of columns and rows


Arrays - store multiple variables in any number of dimensions

Data Frames - tabular data objects consisting of columns and rows

Data Analysis

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