Quantitative data

As explained in Data Without Labels

Quantitative data consists of values that can be measured, weighed, scaled, recorded, and subjected to mathematical operations.

Quantitative data consists of values that can be measured, weighed, scaled, or recorded. It supports mathematical and statistical operations and commonly represents business measures. section 1.2.2

Classify quantitative values as:

• Discrete data: precise integer counts, such as passengers on a plane or a city's population. section 1.2.2
• Continuous data: values within a range that can include decimals, such as height or product price. section 1.2.2

Retail transactions

A structured retail dataset can record customer ID, transaction date, amount spent, number of items, payment mode, and city. Amount and item count are quantitative attributes; payment mode and city are categorical. section 1.2.2

Figure 1.2 The divisions and subdivisions of data

Use the data-classification figure to compare quantitative data with qualitative, structured, and unstructured data. section 1.2.2

How Data Without Labels covers it

How deep it goes

Defines quantitative data and distinguishes discrete integer values from continuous values that may include decimals.

Examples

Uses height, revenue, customer counts, demand quantity, area, volume, and retail transaction amounts as examples.

What you can do after

Evaluate values by identifying whether they are quantitative, discrete, or continuous.

How it connects

Connects quantitative data with qualitative data, discrete data, continuous data, structured data, and unstructured data.

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Also explained in

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  Design for DevelopersQuantitative data is numerical information that can be summarized or analyzed using counts, measurements, or statistics.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA continuous column contains real-number values on a continuous numeric scale.
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  Outlier Detection in PythonA numeric feature is a data attribute whose values are numerical quantities suitable for quantitative comparison and computation.
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  D3.js in Action, Third EditionQuantitative data is information represented by numerical values that can be measured or counted.
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  Delivering Machine Learning Projects: From design to deploymentNumerical data consists of values represented as numbers that support arithmetic comparison and quantitative analysis.
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  Data Storytelling with Altair and AIQuantitative data contains numerical values that represent amounts or measurements.

Compare how 7 books explain quantitative data →

Related concepts

• Objectkind of · java.lang.Object is the root class of Java’s reference-type hierarchy and defines methods inherited by Java reference types.
• integerincludes · An integer is a whole-number value with no fractional part, including negative numbers, zero, and positive numbers.
• floating-point arithmeticincludes · Floating-point arithmetic is computation using finite-precision representations of real numbers, which can introduce rounding and range errors.
• discrete variableincludes · A discrete variable is a variable whose possible values belong to a finite or countable set of distinct alternatives rather than a continuous range.
• meanused by · A mean is a measure of central tendency, calculated as the arithmetic average of values or, for a random variable, as the probability-weighted average of its possible values.

This explainer was generated by Manning's knowledge graph from the text of Data Without Labels. The section links point to the parts of the book it draws on.