Types of Data: Quantitative, Qualitative, and Unstructured

Understanding data is the foundation of all data analytics, machine learning, and advanced data processing. The type, structure, and organization of data determine how it can be analyzed and applied to make informed decisions. In this lesson, we explore how quantitative, qualitative, and unstructured data differ—and how each is used in real-world analytics.

In This Lesson:

Differences between quantitative and qualitative data

Subtypes of quantitative data: discrete and continuous

Subtypes of qualitative data: nominal and ordinal

What unstructured data is and how it’s analyzed

Key Takeaways:

Quantitative vs. Qualitative Data

Quantitative data: Numeric, measurable, used in calculations.

Qualitative data: Descriptive, categorical, focuses on characteristics.
Each requires distinct processing and analysis techniques.

Quantitative Data

Discrete data: Countable, whole-number values (e.g., number of customers).

Continuous data: Measurable, can take on any value within a range (e.g., temperature, time).

Qualitative Data

Nominal data: Categories with no order (e.g., colors, gender, cuisine type).

Ordinal data: Ordered categories, but intervals are not equal (e.g., satisfaction ratings).

Unstructured Data

Data without predefined organization—text, images, audio, video.

Often qualitative, requiring NLP, image recognition, or speech analysis.

Examples include social media posts, product reviews, and videos.

Curriculum Connection

This video is part of Chapter 2 Section 1: Types of Data from Data Rookies: Introduction to Data Analytics, published by Data Analytics Curriculum.

Data Rookies: Introduction to Data Analytics provides students and professionals with a structured introduction to data types, data processing, and analytical thinking. It’s part of the Data Analytics Curriculum, which includes instructor guides, R and Orange lab books, and companion materials.

📘 Access the full curriculum and download labs and materials:
https://dataanalyticscurriculum.com/c...

Who It’s For:
Students, educators, and professionals learning how different data types influence analysis, modeling, and data-driven decision-making.

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