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# System prepended metadata

title: Tipos de dados

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# Tipos de dados

Categorical data: "Categorical data are variables that can be put into categories or groups. These are typically nominal (unordered) or ordinal (ordered) variables" (Schmidt, 2020). Another article defines categorical data as "data that take on one of a limited number of values, such as colors, brands, or types of animals" (Gould, 2019).

Numerical data: "Numerical data can be quantitative, meaning that they represent a quantity or measurement, such as height or weight, or they can be continuous, meaning that they can take on any value within a certain range, such as temperature or time" (Schmidt, 2020). Another article defines numerical data as "data that take on numeric values, such as age, height, or income" (Gould, 2019).

Time-series data: "Time-series data are measurements or observations that are collected over time. Examples of time-series data include stock prices, temperature readings, and website traffic" (Kassambara, 2017). Another article states that "time-series data are data points collected over time that can be analyzed to identify trends, patterns, and anomalies" (Davenport & Kim, 2013).

Spatial data: "Spatial data refer to any type of data that is tied to a specific location or geographic area. This can include things like coordinates, maps, and aerial imagery" (Larsen, 2021). Another article defines spatial data as "data that are referenced to a particular location or set of locations on the earth's surface, such as GPS coordinates, satellite imagery, or land-use data" (Chen, 2013).

Text data: "Text data are any type of data that consists of words or natural language. This can include things like emails, tweets, reviews, and news articles" (Lohr, 2017). Another article defines text data as "data that is represented in natural language form, such as written text, social media posts, or speech transcripts" (Chen, 2013).

*References:
Chen, C. (2013). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171–209.
Davenport, T. H., & Kim, J. (2013). Keeping Up with the Quants: Your Guide to Understanding and Using Analytics. Harvard Business Press.
Gould, J. (2019). Understanding Data Types: Categorical, Ordinal, and Numerical. [Online]. Available: https://www.displayr.com/data-types/.
Kassambara, A. (2017). Introduction to Time Series Analysis. [Online]. Available: https://www.datanovia.com/en/courses/introduction-to-time-series-analysis/.
Larsen, E. (2021). What Is Spatial Data? [Online]. Available: https://www.techopedia.com/definition/32735/spatial-data.
Lohr, S. (2017). Text Mining and Natural Language Processing. [Online]. Available: https://www.nytimes.com/2017/06/12/technology/text-mining-and-natural-language-processing.html.
Schmidt, J. (2020). What Are Categorical, Numerical, and Ordinal Data? [Online]. Available: https://www.statisticshowto.com/categorical-numerical-ordinal-data/.*