Data Analytics: Foundations to Practice · Types and Sources of Data
Quantitative and Qualitative Data
This distinction shapes what analytical techniques a dataset can even support, and confusing the two leads to applying the wrong tool to a dataset entirely. This chapter covers what genuinely separates them.
Quantitative data is inherently numeric and measurable, such as revenue, page views, or a customer's age, and it supports mathematical operations like averaging, summing, and comparing magnitude directly, which is why most of the statistical techniques covered later in this course apply specifically to quantitative data.
Key Takeaways
- Quantitative data is inherently numeric and supports operations like averaging and summing directly.
- Qualitative data is descriptive and categorical; it can be counted but not meaningfully averaged the way numeric data can.
- Ordinal data has a meaningful order but not necessarily equal distances between categories, requiring careful judgment before applying quantitative operations to it.
- Misclassifying data type leads to inappropriate analysis, such as averaging a categorical ID number, which is technically calculable but meaningless.